In this file, we presented the results from analyses of exposures
against computed epigenetic age accelerations (EAA) calculated using DNA
methylation data by different methods. For each exposure, we conducted
both primary analyses (likelihood ratio tests and generalized estimating
equations (GEE), adjusted for confounders) and sensitivity analyses
(likelihood ratio tests and/or generalized estimating equations (GEE)
limited to certain fuel users, not adjusted for confounders). In
addition to what’s included in the analysis plan, we also analyzed
ambient and urinary exposures.
1. Description of study population
There are 129 visits with corresponding epigenetic ages available
among 106 female subjects. For these 106 subjects, 83 have been visited
once and 23 have been visited twice.
The following tables summarize all the information of the first visit
of these 106 subjects.
Baseline characteristics (confounders)
| Characteristic |
N = 106 |
| Age |
56 (15) |
| county |
|
| Fuyuan |
51 / 96 (53%) |
| Xuanwe |
45 / 96 (47%) |
| (Missing) |
10 |
| BMI |
22.0 (3.5) |
| ses |
|
| 0 |
47 / 96 (49%) |
| 1 |
49 / 96 (51%) |
| (Missing) |
10 |
| edu |
|
| 1 |
63 / 96 (66%) |
| 2 |
16 / 96 (17%) |
| 3 |
13 / 96 (14%) |
| 4 |
4 / 96 (4.2%) |
| (Missing) |
10 |
Epigenetic ages
| Characteristic |
N = 106 |
| DNAmAge |
56 (14) |
| DNAmAgeHannum |
59 (14) |
| DNAmPhenoAge |
55 (14) |
| DNAmAgeSkinBloodClock |
56 (13) |
| DNAmGrimAge |
55 (12) |
| DNAmTL |
6.84 (0.33) |
Epigenetic ages accelarations
| Characteristic |
N = 106 |
| AgeAccelerationResidual |
0.2 (4.7) |
| AgeAccelerationResidualHannum |
-0.5 (4.1) |
| AgeAccelPheno |
-0.7 (4.5) |
| DNAmAgeSkinBloodClockAdjAge |
0.0 (3.4) |
| AgeAccelGrim |
-0.31 (3.02) |
| DNAmTLAdjAge |
0.03 (0.18) |
| IEAA |
0.1 (4.4) |
| EEAA |
-0.6 (5.3) |
Fuel/stove type exposures
| Characteristic |
N = 106 |
| curFuel |
|
| Smokeles |
12 / 90 (13%) |
| Smoky |
72 / 90 (80%) |
| Wood_and_or_Plant |
6 / 90 (6.7%) |
| (Missing) |
16 |
| brthFuel |
|
| Mix |
42 / 93 (45%) |
| Smokeles |
3 / 93 (3.2%) |
| Smoky |
40 / 93 (43%) |
| Wood |
8 / 93 (8.6%) |
| (Missing) |
13 |
| cumFuel |
|
| Mix |
64 / 96 (67%) |
| Smoky |
32 / 96 (33%) |
| (Missing) |
10 |
| curStove |
|
| Firepit_and_unventilated |
16 / 90 (18%) |
| Mix |
14 / 90 (16%) |
| Portable_stove |
16 / 90 (18%) |
| Ventilated |
44 / 90 (49%) |
| (Missing) |
16 |
5MC exposures
| Characteristic |
N = 106 |
| cur_5mc |
8.2 (4.2) |
| (Missing) |
12 |
| cum_5mc |
269 (152) |
| (Missing) |
12 |
| bir_5mc |
5.23 (2.83) |
| (Missing) |
12 |
| cur_5mc_measured |
14 (42) |
| (Missing) |
65 |
## [1] "Pearson pair-wise correlation:"
## cur_5mc cum_5mc bir_5mc cur_5mc_measured
## cur_5mc 1.0000000 0.7055284 0.70960439 0.10311689
## cum_5mc 0.7055284 1.0000000 0.84246687 0.17631136
## bir_5mc 0.7096044 0.8424669 1.00000000 -0.04507814
## cur_5mc_measured 0.1031169 0.1763114 -0.04507814 1.00000000
## [1] "Spearman pair-wise correlation:"
## cur_5mc cum_5mc bir_5mc cur_5mc_measured
## cur_5mc 1.0000000 0.6580617 0.6721831 0.4434641
## cum_5mc 0.6580617 1.0000000 0.8313456 0.3335835
## bir_5mc 0.6721831 0.8313456 1.0000000 0.2297288
## cur_5mc_measured 0.4434641 0.3335835 0.2297288 1.0000000
Cluster-based exposures
clusCUR6
Clusters based on model-based exposure estimates at or shortly before
the visit
| Characteristic |
N = 106 |
| CUR6_BC_PAH6 |
0.25 (0.95) |
| (Missing) |
12 |
| CUR6_PAH31 |
0.21 (0.89) |
| (Missing) |
12 |
| CUR6_NkF |
-0.08 (1.08) |
| (Missing) |
12 |
| CUR6_PM_RET |
0.02 (0.93) |
| (Missing) |
12 |
| CUR6_NO2 |
0.15 (0.99) |
| (Missing) |
12 |
| CUR6_SO2 |
-0.18 (0.89) |
| (Missing) |
12 |
clusCHLD5
Clusters based on model-based exposure estimates accrued before age
18
| Characteristic |
N = 106 |
| CHLD5_X7 |
-0.05 (0.87) |
| (Missing) |
12 |
| CHLD5_X33 |
0.15 (0.97) |
| (Missing) |
12 |
| CHLD5_NkF |
-0.11 (1.13) |
| (Missing) |
12 |
| CHLD5_NO2 |
0.18 (1.12) |
| (Missing) |
12 |
| CHLD5_SO2 |
-0.04 (0.88) |
| (Missing) |
12 |
clusCUM6
Clusters based on model-based lifetime exposure estimates
| Characteristic |
N = 106 |
| CUM6_BC_NO2_PM |
0.02 (1.06) |
| (Missing) |
12 |
| CUM6_PAH36 |
0.15 (0.96) |
| (Missing) |
12 |
| CUM6_DlP |
-0.23 (1.04) |
| (Missing) |
12 |
| CUM6_NkF |
-0.09 (1.14) |
| (Missing) |
12 |
| CUM6_RET |
-0.12 (0.97) |
| (Missing) |
12 |
| CUM6_SO2 |
-0.16 (0.92) |
| (Missing) |
12 |
clusMEAS6
Clusters based on pollutant measurements
| Characteristic |
N = 106 |
| MEAS6_BC_PM_RET |
0.15 (0.89) |
| (Missing) |
67 |
| MEAS6_X31 |
0.25 (0.90) |
| (Missing) |
67 |
| MEAS6_X5 |
0.06 (0.98) |
| (Missing) |
67 |
| MEAS6_DlP |
0.06 (1.03) |
| (Missing) |
67 |
| MEAS6_NkF |
0.17 (1.06) |
| (Missing) |
67 |
| MEAS6_NO2_SO2 |
-0.12 (0.90) |
| (Missing) |
67 |
clusURI5
Clusters based on urinary biomarkers
| Characteristic |
N = 106 |
| URI5_NAP_1M_2M |
0.01 (0.97) |
| (Missing) |
13 |
| URI5_ACE |
-0.12 (0.99) |
| (Missing) |
13 |
| URI5_FLU_PHE |
-0.04 (0.96) |
| (Missing) |
13 |
| URI5_PYR |
-0.06 (0.94) |
| (Missing) |
13 |
| URI5_CHR |
-0.02 (1.03) |
| (Missing) |
13 |
Ambient exposures
| Characteristic |
N = 106 |
| bap_air |
66 (91) |
| (Missing) |
3 |
| pm25_air |
205 (188) |
| ANY_air |
908 (1,545) |
| (Missing) |
33 |
| BPE_air |
69 (93) |
| (Missing) |
3 |
| BaA_air |
91 (153) |
| (Missing) |
3 |
| BbF_air |
110 (151) |
| (Missing) |
3 |
| BkF_air |
24 (33) |
| (Missing) |
3 |
| CHR_air |
88 (141) |
| (Missing) |
3 |
| DBA_air |
23 (36) |
| (Missing) |
3 |
| FLT_air |
65 (146) |
| (Missing) |
3 |
| FLU_air |
441 (691) |
| (Missing) |
33 |
| IPY_air |
41 (50) |
| (Missing) |
3 |
| NAP_air |
5,342 (8,071) |
| (Missing) |
33 |
| PHE_air |
675 (1,079) |
| (Missing) |
33 |
| PYR_air |
71 (149) |
| (Missing) |
3 |
Urinary biomarkers
| Characteristic |
N = 106 |
| Benzanthracene_Chrysene_urine |
0.98 (3.49) |
| (Missing) |
2 |
| Naphthalene_urine |
247 (755) |
| Methylnaphthalene_2_urine |
49 (65) |
| (Missing) |
8 |
| Methylnaphthalene_1_urine |
21 (26) |
| (Missing) |
3 |
| Acenaphthene_urine |
8 (11) |
| Phenanthrene_Anthracene_urine |
216 (296) |
| Fluoranthene_urine |
22 (25) |
| Pyrene_urine |
0.74 (0.62) |
| (Missing) |
15 |
2.1. Current (self-reported) fuel type
The numbers of observations with each current fuel type:
##
## Smokeles Smoky Wood_and_or_Plant
## 17 87 8
Primary analysis
Investigate the association with current (self-reported) fuel type in
the LEX study participants, adjusting for known confounders. The
reference group for this analysis would be the smoky coal users. This
would be a categorical analysis, and the results would be a p-value from
the likelihood ratio (LR) test of a confounder-only model to a model
including the exposure variables, as well as p-values for the contrast
of each category of coal use (smokeless coal or plant/wood) to that of
smoky coal. FDR correction should be used separately for each of these
sets. The main interest would be in the coal-specific findings and
perhaps less so in the results from the LR test.
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 *
I(\text{Wood_and_or_Plant}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 *
edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.1398 0.5531
## Hannum EAA 0.4995 0.5531
## PhenoAge EAA 0.4880 0.5531
## Skin&Blood EAA 0.4608 0.5531
## GrimAge EAA 0.0306 0.2448
## DNAmTL 0.4376 0.5531
## IEAA 0.2887 0.5531
## EEAA 0.5531 0.5531
GEE (Mix)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between current fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 *
I(\text{Wood_and_or_Plant}) \\
& + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 *
edu + \epsilon
\end{aligned}
\]
Results:
## The estimated average GrimAge EAA difference of current fuel type, smokeless/wood or plant compared to smoky coal:
## coefficient std ci_lower ci_upper p_val
## Smoky (reference/intercept) 4.5873 1.9055 0.8525 8.3222 0.0161
## Smokeles -1.7864 0.7216 -3.2008 -0.3719 0.0133
## Wood_and_or_Plant 0.6040 1.5782 -2.4893 3.6972 0.7019
## sig_level
## Smoky (reference/intercept) <= 0.05
## Smokeles <= 0.05
## Wood_and_or_Plant > 0.05
Sensitivity analyses
Likelihood ratio (LR) test (no confounders)
Limit the analyses in the primary analysis to include only a single
observation from each subject (no need for a mixed model). The rationale
for this is that it is not so easy to obtain unbiased p-values from a
mixed model for FDR testing. This can be remediated during FDR testing
but would be good to check.
Full model: \[Y = \beta_0 + \beta_1 *
I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) +
\epsilon\] Nested model: \[Y = \beta_0
+ \epsilon\] \(H_0\): The full
model and the nested model fit the data equally well. Thus, you should
use the nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.2800 0.8200
## Hannum EAA 0.4890 0.8819
## PhenoAge EAA 0.8936 0.8936
## Skin&Blood EAA 0.5512 0.8819
## GrimAge EAA 0.1672 0.8200
## DNAmTL 0.8624 0.8936
## IEAA 0.3075 0.8200
## EEAA 0.6635 0.8847
GEE (no confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between current fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 *
I(\text{Wood_and_or_Plant}) + \epsilon
\end{aligned}
\]
Results:
## The estimated average GrimAge EAA difference of current fuel type, smokeless/wood or plant compared to smoky coal:
## coefficient std ci_lower ci_upper p_val
## Smoky (reference/intercept) -0.3652 0.3139 -0.9805 0.2501 0.2447
## Smokeles -1.1498 0.5719 -2.2707 -0.0289 0.0444
## Wood_and_or_Plant 0.4092 1.4927 -2.5166 3.3349 0.7840
## sig_level
## Smoky (reference/intercept) > 0.05
## Smokeles <= 0.05
## Wood_and_or_Plant > 0.05
2.2. Cumulative lifetime (self-reported) fuel type
The numbers of observations with each cumulative lifetime fuel
type:
##
## Mix Smoky
## 82 37
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.1656 0.4416
## Hannum EAA 0.8619 0.9201
## PhenoAge EAA 0.5503 0.8805
## Skin&Blood EAA 0.9201 0.9201
## GrimAge EAA 0.0676 0.2704
## DNAmTL 0.4802 0.8805
## IEAA 0.0532 0.2704
## EEAA 0.8262 0.9201
GEE (Mix)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between cumulative fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\]
Results:
## The estimated average GrimAge EAA difference of cumulative fuel type, mix compared to smoky coal:
## coefficient std ci_lower ci_upper p_val
## Smoky (reference/intercept) 4.2542 1.7709 0.7832 7.7252 0.0163
## Mix -1.0164 0.5314 -2.0578 0.0251 0.0558
## sig_level
## Smoky (reference/intercept) <= 0.05
## Mix > 0.05
Sensitivity analyses
Likelihood ratio (LR) test (no confounders)
Full model: \[Y = \beta_0 + \beta_1 *
I(\text{Mix}) + \epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.3532 0.8414
## Hannum EAA 0.7909 0.8414
## PhenoAge EAA 0.8253 0.8414
## Skin&Blood EAA 0.8414 0.8414
## GrimAge EAA 0.1805 0.7220
## DNAmTL 0.6405 0.8414
## IEAA 0.0759 0.6072
## EEAA 0.6484 0.8414
GEE (no confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between cumulative fuel type and each
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Mix}) + \epsilon
\end{aligned}
\]
Results:
## The estimated average GrimAge EAA difference of cumulative fuel type, mix compared to smoky coal:
## coefficient std ci_lower ci_upper p_val
## Smoky (reference/intercept) 0.0275 0.4057 -0.7676 0.8227 0.9459
## Mix -0.7259 0.5543 -1.8124 0.3606 0.1904
## sig_level
## Smoky (reference/intercept) > 0.05
## Mix > 0.05
2.3. Childhood (self-reported) fuel type
The numbers of observations with each current fuel type:
##
## Mix Smokeles Smoky Wood
## 53 5 47 11
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 *
I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\
& + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 *
edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.1845 0.4920
## Hannum EAA 0.3512 0.5619
## PhenoAge EAA 0.7387 0.7387
## Skin&Blood EAA 0.7259 0.7387
## GrimAge EAA 0.0116 0.0928
## DNAmTL 0.5352 0.7136
## IEAA 0.0811 0.3244
## EEAA 0.3171 0.5619
GEE (Mix)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the association between current fuel type and the Grim
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 *
I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\
& + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 *
edu + \epsilon
\end{aligned}
\]
Results:
## The estimated average GrimAge EAA difference of childhood fuel type, smokeless/wood/mixed compared to smoky coal:
## coefficient std ci_lower ci_upper p_val
## Smoky (reference/intercept) 4.0266 1.7472 0.6022 7.4511 0.0212
## Wood 0.1554 0.8731 -1.5559 1.8668 0.8587
## Smokeles -3.8872 1.1879 -6.2155 -1.5589 0.0011
## Mix -1.4618 0.5666 -2.5724 -0.3512 0.0099
## sig_level
## Smoky (reference/intercept) <= 0.05
## Wood > 0.05
## Smokeles <= 0.01
## Mix <= 0.01
Sensitivity analyses
Likelihood ratio (LR) test (no confounders)
Limit the analyses in the primary analysis to include only a single
observation from each subject (no need for a mixed model). The rationale
for this is that it is not so easy to obtain unbiased p-values from a
mixed model for FDR testing. This can be remediated during FDR testing
but would be good to check.
Full model: \[Y = \beta_0 + \beta_1 *
I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix})
+ \epsilon\] Nested model: \[Y =
\beta_0 + \epsilon\] \(H_0\):
The full model and the nested model fit the data equally well. Thus, you
should use the nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.2833 0.6101
## Hannum EAA 0.3813 0.6101
## PhenoAge EAA 0.8336 0.8336
## Skin&Blood EAA 0.7398 0.8336
## GrimAge EAA 0.0146 0.1168
## DNAmTL 0.5919 0.7892
## IEAA 0.1220 0.4880
## EEAA 0.3340 0.6101
GEE (No confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the association between current fuel type and the Grim
Epigenetic Age Acceleration with the formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 *
I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) + \epsilon
\end{aligned}
\]
Results:
## The estimated average GrimAge EAA difference of childhood fuel type, smokeless/wood/mixed compared to smoky coal:
## coefficient std ci_lower ci_upper p_val
## Smoky (reference/intercept) 0.3085 0.3954 -0.4665 1.0835 0.4353
## Wood 0.1104 0.9417 -1.7353 1.9561 0.9067
## Smokeles -3.8546 1.3488 -6.4982 -1.2109 0.0043
## Mix -1.3960 0.6046 -2.5810 -0.2110 0.0209
## sig_level
## Smoky (reference/intercept) > 0.05
## Wood > 0.05
## Smokeles <= 0.01
## Mix <= 0.05
3.1. Clusters based on model-based exposure estimates at or shortly
before the visit (clusCUR6)
The file “LEX_clusCUR6.csv” has information on current pollutant
exposures, obtained for the 2 years preceding the visit. To reduce
multi-collinearity between exposures, exposure prototypes were derived
based on hierarchical cluster analysis in combination followed by
principal components analysis. These estimates are available for 6
different prototypes (cluster variables) for a total of 161 subjects and
211 visits. The prototypes are labelled as:
CUR6_BC_PAH6 – Black carbon (BC) and 6 PAHs
CUR6_PAH31 – a large cluster of 31 PAHs
CUR6_NkF – NkF only
CUR6_PM_RET – Particulate matter (PM) and retene
CUR6_NO2 – NO2 only
CUR6_SO2 – SO2 only
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| CUR6_BC_PAH6 |
0.79 (-0.5, 0.8) |
-1.32 (-1.4, -0.9) |
0.80 (-0.2, 1.1) |
0.69 (0.1, 0.7) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUR6_PAH31 |
0.38 (-0.4, 0.6) |
-1.14 (-1.4, -0.5) |
0.46 (-0.1, 0.6) |
0.75 (0.4, 0.8) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUR6_NkF |
-0.40 (-0.6, 0.7) |
0.06 (-0.2, 0.3) |
-0.51 (-0.6, 0.9) |
0.74 (-0.2, 0.7) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUR6_PM_RET |
-0.32 (-0.5, 0.4) |
-0.04 (-0.9, 0.3) |
-0.32 (-0.5, 0.1) |
2.49 (0.9, 2.6) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUR6_NO2 |
0.06 (-0.4, 0.8) |
1.00 (0.6, 1.4) |
-0.06 (-0.5, 0.5) |
0.63 (-0.2, 1.3) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUR6_SO2 |
-0.30 (-0.9, 0.3) |
1.37 (0.2, 1.5) |
-0.30 (-0.9, 0.1) |
-1.00 (-1.3, -0.9) |
| (Missing) |
3 |
2 |
1 |
0 |
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31}
+ \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2}
+ \beta_6 * \text{SO2}\\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10}
* edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.1718 0.3058
## Hannum EAA 0.2726 0.3635
## PhenoAge EAA 0.0174 0.0680
## Skin&Blood EAA 0.0255 0.0680
## GrimAge EAA 0.0028 0.0224
## DNAmTL 0.4552 0.4552
## IEAA 0.4430 0.4552
## EEAA 0.1911 0.3058
GEE (Mix)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $CUR6_BC_PAH6
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.6761 0.6262 -1.9035 0.5512 0.2803
## AgeAccelerationResidualHannum -0.3906 0.4991 -1.3688 0.5877 0.4339
## AgeAccelPheno -0.1098 0.4162 -0.9256 0.7059 0.7918
## DNAmAgeSkinBloodClockAdjAge -0.1170 0.4173 -0.9348 0.7008 0.7792
## AgeAccelGrim 0.7687 0.2873 0.2056 1.3318 0.0075
## DNAmTLAdjAge 0.0275 0.0235 -0.0186 0.0736 0.2423
## IEAA -0.2881 0.5474 -1.3611 0.7849 0.5987
## EEAA -0.7446 0.6235 -1.9667 0.4776 0.2324
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PAH31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1799 0.5996 -0.9953 1.3551 0.7641
## AgeAccelerationResidualHannum -0.2175 0.5319 -1.2601 0.8250 0.6826
## AgeAccelPheno 0.0564 0.4237 -0.7741 0.8868 0.8942
## DNAmAgeSkinBloodClockAdjAge 0.3281 0.4025 -0.4608 1.1170 0.4150
## AgeAccelGrim 0.9568 0.2431 0.4803 1.4333 0.0001
## DNAmTLAdjAge -0.0096 0.0180 -0.0448 0.0257 0.5943
## IEAA 0.1555 0.6228 -1.0652 1.3762 0.8029
## EEAA -0.3147 0.6558 -1.6000 0.9707 0.6313
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1361 0.5107 -1.1371 0.8649 0.7899
## AgeAccelerationResidualHannum -0.3542 0.4403 -1.2171 0.5087 0.4211
## AgeAccelPheno -0.5080 0.4155 -1.3224 0.3064 0.2215
## DNAmAgeSkinBloodClockAdjAge -0.1739 0.3811 -0.9209 0.5732 0.6482
## AgeAccelGrim 0.4613 0.2393 -0.0077 0.9304 0.0539
## DNAmTLAdjAge -0.0300 0.0190 -0.0673 0.0072 0.1138
## IEAA -0.1198 0.3900 -0.8842 0.6445 0.7586
## EEAA -0.3215 0.5837 -1.4655 0.8225 0.5817
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2829 0.5422 -0.7797 1.3456 0.6018
## AgeAccelerationResidualHannum -0.5943 0.4604 -1.4967 0.3082 0.1968
## AgeAccelPheno -0.8820 0.4738 -1.8107 0.0468 0.0627
## DNAmAgeSkinBloodClockAdjAge -0.5316 0.5129 -1.5370 0.4737 0.3000
## AgeAccelGrim 0.7117 0.4002 -0.0727 1.4961 0.0754
## DNAmTLAdjAge 0.0012 0.0248 -0.0474 0.0499 0.9605
## IEAA 0.4740 0.4784 -0.4636 1.4116 0.3218
## EEAA -0.6387 0.6418 -1.8967 0.6193 0.3197
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.6950 0.5078 -0.3002 1.6903 0.1711
## AgeAccelerationResidualHannum -0.0270 0.4125 -0.8355 0.7815 0.9479
## AgeAccelPheno -0.0108 0.4477 -0.8883 0.8667 0.9807
## DNAmAgeSkinBloodClockAdjAge 0.3085 0.3884 -0.4528 1.0698 0.4270
## AgeAccelGrim -0.0321 0.2850 -0.5907 0.5265 0.9104
## DNAmTLAdjAge 0.0137 0.0180 -0.0216 0.0490 0.4457
## IEAA 0.4456 0.4447 -0.4260 1.3172 0.3164
## EEAA 0.0012 0.5778 -1.1313 1.1336 0.9984
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2870 0.5430 -1.3514 0.7774 0.5972
## AgeAccelerationResidualHannum -0.4429 0.5619 -1.5442 0.6585 0.4306
## AgeAccelPheno -0.4050 0.5353 -1.4541 0.6442 0.4493
## DNAmAgeSkinBloodClockAdjAge -0.4975 0.4795 -1.4373 0.4423 0.2995
## AgeAccelGrim -0.6569 0.3359 -1.3153 0.0014 0.0505
## DNAmTLAdjAge 0.0021 0.0212 -0.0394 0.0437 0.9197
## IEAA -0.5388 0.4975 -1.5140 0.4364 0.2788
## EEAA -0.6422 0.6638 -1.9433 0.6588 0.3333
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (Mix, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_PAH6 + \beta_2 PAH31 + \beta_3 NkF
+ \beta_4 PM\_RET + \beta_5 NO2 + \beta_6 SO2 \\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## The estimated effects:
## $CUR6_BC_PAH6
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.6422 1.0352 -3.6713 0.3868 0.1127
## AgeAccelerationResidualHannum -1.1232 0.7420 -2.5776 0.3311 0.1301
## AgeAccelPheno -1.0110 0.6283 -2.2425 0.2204 0.1076
## DNAmAgeSkinBloodClockAdjAge -0.9548 0.7991 -2.5211 0.6114 0.2321
## AgeAccelGrim 0.6418 0.4580 -0.2559 1.5395 0.1611
## DNAmTLAdjAge 0.0348 0.0327 -0.0293 0.0989 0.2871
## IEAA -0.9920 0.7357 -2.4341 0.4501 0.1776
## EEAA -1.7435 0.9797 -3.6637 0.1767 0.0751
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PAH31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.0694 0.9220 -0.7378 2.8766 0.2461
## AgeAccelerationResidualHannum 0.8481 0.8262 -0.7714 2.4675 0.3047
## AgeAccelPheno 1.4674 0.6556 0.1825 2.7523 0.0252
## DNAmAgeSkinBloodClockAdjAge 1.4658 0.7189 0.0567 2.8749 0.0415
## AgeAccelGrim 0.3424 0.4309 -0.5021 1.1869 0.4268
## DNAmTLAdjAge -0.0237 0.0288 -0.0800 0.0327 0.4109
## IEAA 0.4782 0.8722 -1.2314 2.1877 0.5835
## EEAA 1.0678 1.0463 -0.9830 3.1186 0.3075
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.8479 0.8031 -2.4221 0.7262 0.2911
## AgeAccelerationResidualHannum -0.4910 0.6889 -1.8412 0.8593 0.4760
## AgeAccelPheno -0.6177 0.5169 -1.6308 0.3955 0.2321
## DNAmAgeSkinBloodClockAdjAge -0.3074 0.6923 -1.6643 1.0494 0.6570
## AgeAccelGrim 0.5426 0.3387 -0.1213 1.2065 0.1092
## DNAmTLAdjAge -0.0251 0.0225 -0.0693 0.0190 0.2648
## IEAA -0.5726 0.5374 -1.6259 0.4806 0.2866
## EEAA -0.5814 0.8763 -2.2989 1.1362 0.5071
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1184 0.7164 -1.2858 1.5226 0.8687
## AgeAccelerationResidualHannum -0.8038 0.6292 -2.0370 0.4294 0.2014
## AgeAccelPheno -1.4086 0.5999 -2.5843 -0.2328 0.0189
## DNAmAgeSkinBloodClockAdjAge -1.2856 0.6607 -2.5806 0.0094 0.0517
## AgeAccelGrim 0.0996 0.4475 -0.7774 0.9767 0.8238
## DNAmTLAdjAge 0.0203 0.0301 -0.0387 0.0793 0.5002
## IEAA 0.4365 0.6702 -0.8772 1.7501 0.5149
## EEAA -0.9111 0.8335 -2.5447 0.7225 0.2743
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.6432 0.5327 -0.4008 1.6872 0.2272
## AgeAccelerationResidualHannum 0.1791 0.4689 -0.7400 1.0982 0.7025
## AgeAccelPheno 0.2979 0.4674 -0.6181 1.2140 0.5238
## DNAmAgeSkinBloodClockAdjAge 0.6087 0.4370 -0.2478 1.4651 0.1636
## AgeAccelGrim 0.0661 0.3025 -0.5268 0.6590 0.8271
## DNAmTLAdjAge 0.0158 0.0184 -0.0203 0.0519 0.3903
## IEAA 0.4395 0.4653 -0.4725 1.3516 0.3449
## EEAA 0.2371 0.5973 -0.9335 1.4078 0.6913
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.8092 0.6830 -2.1479 0.5295 0.2361
## AgeAccelerationResidualHannum -0.8968 0.6305 -2.1326 0.3390 0.1549
## AgeAccelPheno -0.8388 0.4924 -1.8039 0.1263 0.0885
## DNAmAgeSkinBloodClockAdjAge -1.0696 0.5518 -2.1511 0.0119 0.0526
## AgeAccelGrim -0.4987 0.2868 -1.0607 0.0634 0.0820
## DNAmTLAdjAge 0.0189 0.0229 -0.0260 0.0638 0.4087
## IEAA -0.7972 0.5958 -1.9649 0.3706 0.1809
## EEAA -1.3413 0.7417 -2.7951 0.1124 0.0705
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
Likelihood ratio (LR) test (no confounders)
Full model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 *
\text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 *
\text{SO2}\\
& + \epsilon
\end{aligned}
\] Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.1840 0.2968
## Hannum EAA 0.2914 0.3775
## PhenoAge EAA 0.0241 0.0755
## Skin&Blood EAA 0.0283 0.0755
## GrimAge EAA 0.0263 0.0755
## DNAmTL 0.4823 0.4823
## IEAA 0.3303 0.3775
## EEAA 0.1855 0.2968
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 *
\text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 *
\text{SO2}\\
& + \epsilon
\end{aligned}
\] Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.4226 0.4830
## Hannum EAA 0.1558 0.2707
## PhenoAge EAA 0.0209 0.1672
## Skin&Blood EAA 0.1692 0.2707
## GrimAge EAA 0.0806 0.2149
## DNAmTL 0.2510 0.3347
## IEAA 0.6041 0.6041
## EEAA 0.0626 0.2149
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 *
\text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 *
\text{SO2}\\
& + \epsilon
\end{aligned}
\] Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0700 0.0800
## Hannum EAA 0.0037 0.0148
## PhenoAge EAA 0.0093 0.0248
## Skin&Blood EAA 0.0426 0.0800
## GrimAge EAA 0.0651 0.0800
## DNAmTL 0.2166 0.2166
## IEAA 0.0509 0.0800
## EEAA 0.0019 0.0148
GEE (No confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $CUR6_BC_PAH6
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.6520 0.5848 -1.7983 0.4942 0.2649
## AgeAccelerationResidualHannum -0.3481 0.4333 -1.1973 0.5011 0.4217
## AgeAccelPheno -0.2974 0.4152 -1.1111 0.5163 0.4737
## DNAmAgeSkinBloodClockAdjAge -0.1340 0.3940 -0.9063 0.6383 0.7338
## AgeAccelGrim 0.4438 0.2635 -0.0726 0.9603 0.0921
## DNAmTLAdjAge 0.0330 0.0199 -0.0059 0.0720 0.0965
## IEAA -0.3293 0.5098 -1.3286 0.6700 0.5184
## EEAA -0.6468 0.5313 -1.6883 0.3946 0.2235
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PAH31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1849 0.5865 -0.9646 1.3345 0.7525
## AgeAccelerationResidualHannum -0.1720 0.4903 -1.1329 0.7890 0.7258
## AgeAccelPheno -0.0874 0.4312 -0.9325 0.7578 0.8395
## DNAmAgeSkinBloodClockAdjAge 0.2744 0.3991 -0.5077 1.0566 0.4916
## AgeAccelGrim 0.8389 0.2723 0.3051 1.3727 0.0021
## DNAmTLAdjAge -0.0048 0.0184 -0.0408 0.0312 0.7945
## IEAA 0.1455 0.6018 -1.0340 1.3249 0.8090
## EEAA -0.2466 0.5932 -1.4093 0.9161 0.6776
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0032 0.5124 -1.0011 1.0076 0.9950
## AgeAccelerationResidualHannum -0.2888 0.4195 -1.1110 0.5335 0.4912
## AgeAccelPheno -0.4877 0.4246 -1.3200 0.3446 0.2508
## DNAmAgeSkinBloodClockAdjAge -0.1578 0.3648 -0.8727 0.5572 0.6654
## AgeAccelGrim 0.5243 0.2651 0.0046 1.0440 0.0480
## DNAmTLAdjAge -0.0325 0.0183 -0.0684 0.0034 0.0759
## IEAA -0.0481 0.4058 -0.8434 0.7472 0.9056
## EEAA -0.2151 0.5626 -1.3178 0.8877 0.7023
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3099 0.5359 -0.7404 1.3603 0.5630
## AgeAccelerationResidualHannum -0.5576 0.4625 -1.4641 0.3489 0.2280
## AgeAccelPheno -0.7742 0.4994 -1.7530 0.2047 0.1211
## DNAmAgeSkinBloodClockAdjAge -0.5419 0.5002 -1.5222 0.4385 0.2787
## AgeAccelGrim 0.8264 0.4161 0.0109 1.6419 0.0470
## DNAmTLAdjAge -0.0027 0.0235 -0.0488 0.0434 0.9087
## IEAA 0.5748 0.4840 -0.3739 1.5235 0.2350
## EEAA -0.6345 0.6319 -1.8729 0.6040 0.3153
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4583 0.4309 -0.3863 1.3030 0.2875
## AgeAccelerationResidualHannum -0.0198 0.3680 -0.7410 0.7015 0.9572
## AgeAccelPheno 0.2655 0.3812 -0.4817 1.0128 0.4861
## DNAmAgeSkinBloodClockAdjAge 0.2463 0.3083 -0.3580 0.8506 0.4243
## AgeAccelGrim 0.1308 0.3115 -0.4797 0.7413 0.6745
## DNAmTLAdjAge 0.0010 0.0156 -0.0295 0.0316 0.9478
## IEAA 0.4146 0.4083 -0.3857 1.2149 0.3099
## EEAA -0.0830 0.4994 -1.0618 0.8959 0.8680
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUR6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.3123 0.5157 -1.3230 0.6985 0.5448
## AgeAccelerationResidualHannum -0.3725 0.5345 -1.4200 0.6751 0.4859
## AgeAccelPheno -0.1697 0.5410 -1.2300 0.8906 0.7538
## DNAmAgeSkinBloodClockAdjAge -0.4321 0.4484 -1.3110 0.4468 0.3352
## AgeAccelGrim -0.4874 0.3124 -1.0997 0.1250 0.1188
## DNAmTLAdjAge -0.0070 0.0195 -0.0452 0.0311 0.7174
## IEAA -0.4773 0.4994 -1.4561 0.5016 0.3393
## EEAA -0.5823 0.6174 -1.7923 0.6278 0.3456
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
3.2. Clusters based on model-based exposure estimates accrued before
age 18 (clusCHLD5)
The file “LEX_clusCHLD5.csv” has information on estimated pollutant
exposures during early childhood. Estimates are available for 5
different prototypes (cluster variables) for a total of 161 subjects and
211 visits. The prototypes are labelled as:
CHLD5_X7 – a cluster of 7 air pollutants
CHLD5_X33 – a large cluster of 33 air pollutants
CHLD5_NkF – NkF only
CHLD5_NO2 – NO2 only
CHLD5_SO2 – SO2 only
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| CHLD5_X7 |
0.09 (-0.5, 0.5) |
-0.63 (-0.9, -0.1) |
0.10 (-0.5, 0.3) |
0.86 (0.7, 1.1) |
| (Missing) |
3 |
2 |
1 |
0 |
| CHLD5_X33 |
0.23 (-0.7, 1.1) |
-0.83 (-1.4, 0.1) |
0.51 (-0.4, 1.2) |
0.95 (-0.1, 1.0) |
| (Missing) |
3 |
2 |
1 |
0 |
| CHLD5_NkF |
-0.21 (-0.8, 0.7) |
0.06 (-0.3, 0.7) |
-0.45 (-1.0, 0.5) |
1.07 (0.5, 1.5) |
| (Missing) |
3 |
2 |
1 |
0 |
| CHLD5_NO2 |
0.34 (-0.5, 0.8) |
0.17 (-0.5, 0.9) |
0.43 (-0.6, 0.8) |
-0.21 (-0.3, 0.2) |
| (Missing) |
3 |
2 |
1 |
0 |
| CHLD5_SO2 |
0.34 (-0.7, 0.4) |
0.45 (0.3, 1.4) |
0.34 (-0.9, 0.4) |
0.22 (-0.2, 0.3) |
| (Missing) |
3 |
2 |
1 |
0 |
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} +
\beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2}\\
& + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} *
edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.7432 0.7432
## Hannum EAA 0.0942 0.1884
## PhenoAge EAA 0.0854 0.1884
## Skin&Blood EAA 0.1751 0.2802
## GrimAge EAA 0.0062 0.0496
## DNAmTL 0.7396 0.7432
## IEAA 0.5899 0.7432
## EEAA 0.0758 0.1884
GEE (Mix)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $CHLD5_X7
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3917 0.6187 -0.8211 1.6044 0.5267
## AgeAccelerationResidualHannum 0.7213 0.5532 -0.3629 1.8056 0.1923
## AgeAccelPheno 0.3717 0.4627 -0.5352 1.2785 0.4218
## DNAmAgeSkinBloodClockAdjAge 0.2363 0.5132 -0.7695 1.2422 0.6452
## AgeAccelGrim 0.8467 0.2817 0.2945 1.3989 0.0027
## DNAmTLAdjAge -0.0045 0.0223 -0.0481 0.0392 0.8412
## IEAA 0.2322 0.5680 -0.8812 1.3455 0.6827
## EEAA 1.1347 0.6785 -0.1951 2.4645 0.0944
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_X33
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1259 0.5224 -0.8980 1.1498 0.8095
## AgeAccelerationResidualHannum 0.8465 0.5130 -0.1590 1.8519 0.0989
## AgeAccelPheno 1.1737 0.4197 0.3512 1.9963 0.0052
## DNAmAgeSkinBloodClockAdjAge 0.8201 0.4060 0.0243 1.6159 0.0434
## AgeAccelGrim 0.9856 0.2866 0.4240 1.5473 0.0006
## DNAmTLAdjAge -0.0027 0.0198 -0.0415 0.0362 0.8937
## IEAA -0.3685 0.5105 -1.3691 0.6320 0.4704
## EEAA 1.1985 0.6145 -0.0060 2.4029 0.0511
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.01
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1931 0.3349 -0.4633 0.8496 0.5642
## AgeAccelerationResidualHannum 0.2603 0.2862 -0.3007 0.8213 0.3631
## AgeAccelPheno -0.1291 0.3552 -0.8254 0.5672 0.7163
## DNAmAgeSkinBloodClockAdjAge -0.1611 0.3120 -0.7727 0.4504 0.6056
## AgeAccelGrim 0.4115 0.2526 -0.0836 0.9066 0.1033
## DNAmTLAdjAge -0.0192 0.0192 -0.0568 0.0184 0.3158
## IEAA 0.0420 0.3012 -0.5483 0.6323 0.8892
## EEAA 0.3784 0.3929 -0.3917 1.1485 0.3355
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0696 0.3649 -0.7848 0.6456 0.8488
## AgeAccelerationResidualHannum 0.2628 0.3637 -0.4502 0.9757 0.4700
## AgeAccelPheno 0.3558 0.3436 -0.3176 1.0292 0.3004
## DNAmAgeSkinBloodClockAdjAge 0.4472 0.2986 -0.1381 1.0326 0.1343
## AgeAccelGrim -0.0598 0.2119 -0.4750 0.3555 0.7779
## DNAmTLAdjAge -0.0064 0.0147 -0.0353 0.0225 0.6655
## IEAA -0.1213 0.3125 -0.7338 0.4911 0.6978
## EEAA 0.3002 0.4426 -0.5673 1.1677 0.4976
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1094 0.6905 -1.2439 1.4627 0.8741
## AgeAccelerationResidualHannum 0.3280 0.5959 -0.8399 1.4959 0.5820
## AgeAccelPheno 0.6419 0.5146 -0.3667 1.6506 0.2123
## DNAmAgeSkinBloodClockAdjAge 0.2753 0.5373 -0.7779 1.3285 0.6084
## AgeAccelGrim -0.0645 0.3084 -0.6690 0.5399 0.8343
## DNAmTLAdjAge 0.0120 0.0229 -0.0329 0.0568 0.6005
## IEAA -0.0170 0.5925 -1.1783 1.1442 0.9771
## EEAA 0.2154 0.7413 -1.2376 1.6684 0.7714
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (Mix, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X7 + \beta_2 X33 + \beta_3 NkF +
\beta_4 NO2 + \beta_5 SO2 \\
& + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_9 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## The estimated effects:
## $CHLD5_X7
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.6904 1.0225 -1.3137 2.6945 0.4995
## AgeAccelerationResidualHannum 0.6243 0.8139 -0.9710 2.2195 0.4431
## AgeAccelPheno -0.1287 0.7865 -1.6703 1.4128 0.8700
## DNAmAgeSkinBloodClockAdjAge -0.1970 0.8598 -1.8822 1.4881 0.8188
## AgeAccelGrim 0.1983 0.4228 -0.6304 1.0269 0.6391
## DNAmTLAdjAge 0.0165 0.0338 -0.0497 0.0827 0.6248
## IEAA 1.0194 0.8572 -0.6608 2.6995 0.2344
## EEAA 0.9046 1.0422 -1.1381 2.9472 0.3854
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_X33
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2729 0.8576 -1.9538 1.4079 0.7503
## AgeAccelerationResidualHannum 0.4460 0.6396 -0.8077 1.6996 0.4856
## AgeAccelPheno 1.2061 0.6459 -0.0599 2.4721 0.0619
## DNAmAgeSkinBloodClockAdjAge 0.8990 0.6841 -0.4418 2.2398 0.1888
## AgeAccelGrim 0.8611 0.3610 0.1536 1.5686 0.0171
## DNAmTLAdjAge -0.0106 0.0297 -0.0688 0.0475 0.7198
## IEAA -0.9280 0.7195 -2.3382 0.4822 0.1971
## EEAA 0.6388 0.8157 -0.9600 2.2377 0.4335
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0648 0.3457 -0.6127 0.7423 0.8513
## AgeAccelerationResidualHannum 0.1630 0.3172 -0.4588 0.7848 0.6073
## AgeAccelPheno -0.0802 0.3644 -0.7945 0.6340 0.8257
## DNAmAgeSkinBloodClockAdjAge -0.0941 0.2975 -0.6771 0.4889 0.7518
## AgeAccelGrim 0.3179 0.2550 -0.1819 0.8177 0.2125
## DNAmTLAdjAge -0.0227 0.0206 -0.0631 0.0178 0.2717
## IEAA -0.1412 0.3156 -0.7598 0.4775 0.6547
## EEAA 0.2071 0.4338 -0.6432 1.0573 0.6331
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0677 0.4255 -0.9017 0.7662 0.8735
## AgeAccelerationResidualHannum 0.1980 0.3563 -0.5003 0.8964 0.5783
## AgeAccelPheno 0.0596 0.3624 -0.6507 0.7699 0.8693
## DNAmAgeSkinBloodClockAdjAge 0.3526 0.3091 -0.2532 0.9583 0.2540
## AgeAccelGrim -0.0558 0.2133 -0.4738 0.3623 0.7938
## DNAmTLAdjAge -0.0142 0.0186 -0.0506 0.0223 0.4471
## IEAA -0.0383 0.3904 -0.8036 0.7269 0.9218
## EEAA 0.2944 0.4404 -0.5687 1.1576 0.5038
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4651 0.8224 -1.1467 2.0769 0.5717
## AgeAccelerationResidualHannum 0.4725 0.7251 -0.9487 1.8936 0.5147
## AgeAccelPheno 0.4258 0.5653 -0.6821 1.5337 0.4513
## DNAmAgeSkinBloodClockAdjAge -0.0995 0.5498 -1.1770 0.9780 0.8564
## AgeAccelGrim 0.0243 0.3633 -0.6878 0.7364 0.9467
## DNAmTLAdjAge 0.0230 0.0298 -0.0353 0.0813 0.4396
## IEAA 0.4772 0.7482 -0.9893 1.9437 0.5236
## EEAA 0.4131 0.8874 -1.3263 2.1524 0.6416
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
Likelihood ratio (LR) test (no confounders)
Full model: \[Y = \beta_0 + \beta_1 *
\text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 *
\text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.8864 0.8864
## Hannum EAA 0.2901 0.5840
## PhenoAge EAA 0.1416 0.5664
## Skin&Blood EAA 0.3650 0.5840
## GrimAge EAA 0.0208 0.1664
## DNAmTL 0.5466 0.7288
## IEAA 0.6847 0.7825
## EEAA 0.3074 0.5840
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[Y = \beta_0 + \beta_1 *
\text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 *
\text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.9805 0.9805
## Hannum EAA 0.3840 0.7341
## PhenoAge EAA 0.0700 0.2800
## Skin&Blood EAA 0.1867 0.4979
## GrimAge EAA 0.0634 0.2800
## DNAmTL 0.5506 0.7341
## IEAA 0.7515 0.8589
## EEAA 0.4588 0.7341
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[Y = \beta_0 + \beta_1 *
\text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 *
\text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.9268 0.9268
## Hannum EAA 0.3979 0.6366
## PhenoAge EAA 0.0655 0.3264
## Skin&Blood EAA 0.2424 0.6366
## GrimAge EAA 0.0816 0.3264
## DNAmTL 0.3621 0.6366
## IEAA 0.7955 0.9091
## EEAA 0.4873 0.6497
GEE (no confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $CHLD5_X7
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2300 0.6355 -1.0156 1.4756 0.7175
## AgeAccelerationResidualHannum 0.6428 0.5141 -0.3647 1.6504 0.2111
## AgeAccelPheno 0.3259 0.4793 -0.6135 1.2652 0.4965
## DNAmAgeSkinBloodClockAdjAge 0.1892 0.5195 -0.8290 1.2073 0.7157
## AgeAccelGrim 0.7565 0.3067 0.1554 1.3576 0.0136
## DNAmTLAdjAge -0.0004 0.0214 -0.0422 0.0415 0.9869
## IEAA 0.1529 0.5995 -1.0221 1.3279 0.7987
## EEAA 0.9809 0.6373 -0.2683 2.2300 0.1238
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_X33
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0284 0.4894 -0.9875 0.9307 0.9537
## AgeAccelerationResidualHannum 0.6433 0.4653 -0.2686 1.5552 0.1668
## AgeAccelPheno 0.9367 0.3985 0.1556 1.7178 0.0188
## DNAmAgeSkinBloodClockAdjAge 0.6423 0.3643 -0.0718 1.3564 0.0779
## AgeAccelGrim 0.7756 0.2960 0.1954 1.3558 0.0088
## DNAmTLAdjAge 0.0060 0.0177 -0.0287 0.0406 0.7361
## IEAA -0.3400 0.4841 -1.2889 0.6089 0.4825
## EEAA 0.8631 0.5639 -0.2422 1.9683 0.1259
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2009 0.3508 -0.4867 0.8885 0.5669
## AgeAccelerationResidualHannum 0.2858 0.2990 -0.3002 0.8718 0.3390
## AgeAccelPheno -0.0177 0.3683 -0.7395 0.7041 0.9617
## DNAmAgeSkinBloodClockAdjAge -0.1422 0.2889 -0.7085 0.4242 0.6227
## AgeAccelGrim 0.4860 0.2742 -0.0515 1.0235 0.0764
## DNAmTLAdjAge -0.0246 0.0172 -0.0582 0.0091 0.1520
## IEAA 0.0679 0.3115 -0.5425 0.6784 0.8273
## EEAA 0.3955 0.4037 -0.3958 1.1869 0.3272
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_NO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2704 0.3266 -0.9105 0.3698 0.4078
## AgeAccelerationResidualHannum 0.1426 0.3101 -0.4653 0.7505 0.6457
## AgeAccelPheno 0.4990 0.3155 -0.1194 1.1174 0.1137
## DNAmAgeSkinBloodClockAdjAge 0.3625 0.2584 -0.1440 0.8690 0.1607
## AgeAccelGrim -0.0149 0.1829 -0.3734 0.3436 0.9349
## DNAmTLAdjAge -0.0077 0.0140 -0.0352 0.0197 0.5795
## IEAA -0.1196 0.2780 -0.6646 0.4253 0.6670
## EEAA 0.0513 0.3955 -0.7239 0.8265 0.8968
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CHLD5_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2091 0.6424 -1.4682 1.0500 0.7448
## AgeAccelerationResidualHannum 0.2457 0.4857 -0.7062 1.1976 0.6129
## AgeAccelPheno 0.7646 0.4714 -0.1594 1.6886 0.1048
## DNAmAgeSkinBloodClockAdjAge 0.2308 0.4446 -0.6406 1.1022 0.6037
## AgeAccelGrim -0.1219 0.2866 -0.6836 0.4399 0.6707
## DNAmTLAdjAge 0.0078 0.0210 -0.0333 0.0490 0.7090
## IEAA -0.1002 0.5718 -1.2210 1.0205 0.8608
## EEAA 0.0178 0.6093 -1.1765 1.2121 0.9767
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
3.3. Clusters based on model-based lifetime exposure estimates
(clusCUM6)
The file “LEX_clus CUM6.csv” has information on estimated cumulative
pollutant exposures during the lifecourse. Estimates are available for 6
different prototypes (cluster variables) for a total of 161 subjects and
211 visits. The prototypes are labelled as:
CUM6_BC_NO2_PM – a cluster of BC, NO2, and PM
CUM6_PAH36 – a large cluster of 36 PAHs
CUM6_DlP – DlP only
CUM6_NkF – NkF only
CUM6_RET – retene only
CUM6_SO2 – SO2 only
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| CUM6_BC_NO2_PM |
0.22 (-0.6, 0.8) |
0.19 (-0.3, 0.7) |
0.10 (-1.0, 0.8) |
1.38 (0.4, 1.6) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUM6_PAH36 |
0.25 (-0.6, 1.1) |
-1.00 (-1.2, -0.3) |
0.32 (-0.5, 1.2) |
0.83 (0.4, 1.4) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUM6_DlP |
-0.48 (-1.0, 0.8) |
0.65 (0.5, 1.1) |
-0.66 (-1.2, 0.7) |
0.42 (0.3, 0.6) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUM6_NkF |
-0.22 (-0.8, 0.5) |
-0.07 (-0.3, 0.4) |
-0.31 (-1.0, 0.4) |
1.18 (0.1, 1.7) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUM6_RET |
-0.22 (-0.7, 0.3) |
-0.41 (-0.9, 0.3) |
-0.25 (-0.8, 0.2) |
1.71 (1.2, 1.9) |
| (Missing) |
3 |
2 |
1 |
0 |
| CUM6_SO2 |
0.09 (-0.4, 0.4) |
1.13 (0.5, 1.6) |
-0.03 (-0.9, 0.3) |
-0.02 (-0.6, 0.1) |
| (Missing) |
3 |
2 |
1 |
0 |
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 *
\text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 *
\text{RET} + \beta_6 * \text{SO2}\\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10}
* edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.4352 0.4352
## Hannum EAA 0.3483 0.3981
## PhenoAge EAA 0.1067 0.3406
## Skin&Blood EAA 0.1656 0.3406
## GrimAge EAA 0.0003 0.0024
## DNAmTL 0.1703 0.3406
## IEAA 0.2877 0.3836
## EEAA 0.2428 0.3836
GEE (Mix)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $CUM6_BC_NO2_PM
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4392 0.6311 -0.7979 1.6762 0.4865
## AgeAccelerationResidualHannum 0.8076 0.5831 -0.3353 1.9506 0.1661
## AgeAccelPheno 0.2404 0.5971 -0.9300 1.4107 0.6873
## DNAmAgeSkinBloodClockAdjAge 0.0290 0.6440 -1.2332 1.2913 0.9640
## AgeAccelGrim 0.8858 0.3820 0.1371 1.6345 0.0204
## DNAmTLAdjAge -0.0262 0.0205 -0.0663 0.0139 0.2010
## IEAA 0.5180 0.6224 -0.7019 1.7378 0.4053
## EEAA 1.2115 0.7200 -0.1997 2.6226 0.0924
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_PAH36
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3210 0.6119 -0.8784 1.5204 0.5999
## AgeAccelerationResidualHannum 0.7980 0.5509 -0.2818 1.8778 0.1475
## AgeAccelPheno 0.8835 0.4908 -0.0785 1.8455 0.0719
## DNAmAgeSkinBloodClockAdjAge 0.6827 0.4676 -0.2339 1.5993 0.1443
## AgeAccelGrim 1.2678 0.2661 0.7462 1.7895 0.0000
## DNAmTLAdjAge -0.0178 0.0224 -0.0617 0.0262 0.4278
## IEAA 0.1289 0.5821 -1.0121 1.2699 0.8247
## EEAA 1.1098 0.6575 -0.1790 2.3986 0.0914
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.7756 0.5383 -0.2795 1.8306 0.1496
## AgeAccelerationResidualHannum 0.1327 0.5475 -0.9404 1.2058 0.8085
## AgeAccelPheno 0.1314 0.4237 -0.6991 0.9619 0.7565
## DNAmAgeSkinBloodClockAdjAge 0.1594 0.4375 -0.6982 1.0170 0.7157
## AgeAccelGrim -0.5186 0.2508 -1.0102 -0.0270 0.0387
## DNAmTLAdjAge -0.0332 0.0204 -0.0731 0.0068 0.1037
## IEAA 0.7906 0.4787 -0.1476 1.7288 0.0986
## EEAA 0.3262 0.6815 -1.0095 1.6620 0.6322
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2349 0.3700 -0.4904 0.9602 0.5256
## AgeAccelerationResidualHannum 0.1825 0.3847 -0.5715 0.9366 0.6352
## AgeAccelPheno -0.1376 0.4010 -0.9235 0.6484 0.7316
## DNAmAgeSkinBloodClockAdjAge -0.1358 0.3705 -0.8619 0.5903 0.7139
## AgeAccelGrim 0.6442 0.2341 0.1853 1.1031 0.0059
## DNAmTLAdjAge -0.0386 0.0179 -0.0736 -0.0035 0.0309
## IEAA 0.1012 0.3225 -0.5309 0.7333 0.7536
## EEAA 0.3622 0.5060 -0.6296 1.3541 0.4741
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2835 0.5528 -0.7999 1.3669 0.6080
## AgeAccelerationResidualHannum -0.0439 0.4393 -0.9049 0.8172 0.9205
## AgeAccelPheno -0.4557 0.4564 -1.3503 0.4388 0.3180
## DNAmAgeSkinBloodClockAdjAge -0.3540 0.5068 -1.3473 0.6394 0.4849
## AgeAccelGrim 0.7790 0.3676 0.0584 1.4995 0.0341
## DNAmTLAdjAge -0.0109 0.0214 -0.0529 0.0310 0.6095
## IEAA 0.4603 0.4813 -0.4831 1.4037 0.3389
## EEAA 0.0952 0.5937 -1.0685 1.2588 0.8726
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1814 0.6632 -1.4812 1.1185 0.7845
## AgeAccelerationResidualHannum 0.0367 0.6422 -1.2219 1.2954 0.9544
## AgeAccelPheno 0.2268 0.5123 -0.7773 1.2309 0.6580
## DNAmAgeSkinBloodClockAdjAge -0.1203 0.5488 -1.1958 0.9553 0.8265
## AgeAccelGrim -0.3276 0.3323 -0.9790 0.3238 0.3242
## DNAmTLAdjAge 0.0204 0.0237 -0.0260 0.0668 0.3897
## IEAA -0.0504 0.5732 -1.1739 1.0730 0.9299
## EEAA -0.2601 0.7587 -1.7472 1.2270 0.7317
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (Mix, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_NO2\_PM + \beta_2 PAH36 + \beta_3
DlP + \beta_4 NkF + \beta_5 RET + \beta_6 SO2 \\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## The estimated effects:
## $CUM6_BC_NO2_PM
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.3294 0.6969 -1.6953 1.0365 0.6365
## AgeAccelerationResidualHannum 0.7298 0.7670 -0.7736 2.2332 0.3414
## AgeAccelPheno -0.4609 0.7514 -1.9336 1.0117 0.5396
## DNAmAgeSkinBloodClockAdjAge -0.7105 0.7201 -2.1220 0.7009 0.3238
## AgeAccelGrim 0.4986 0.4906 -0.4630 1.4602 0.3095
## DNAmTLAdjAge -0.0252 0.0304 -0.0848 0.0344 0.4070
## IEAA -0.2544 0.7423 -1.7092 1.2005 0.7318
## EEAA 1.0705 0.9587 -0.8087 2.9496 0.2642
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_PAH36
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.9063 0.8081 -0.6777 2.4902 0.2621
## AgeAccelerationResidualHannum 0.7311 0.7755 -0.7889 2.2511 0.3458
## AgeAccelPheno 1.9266 0.7208 0.5139 3.3394 0.0075
## DNAmAgeSkinBloodClockAdjAge 1.7831 0.7917 0.2313 3.3349 0.0243
## AgeAccelGrim 0.5738 0.4859 -0.3785 1.5261 0.2376
## DNAmTLAdjAge -0.0070 0.0335 -0.0727 0.0587 0.8344
## IEAA 0.5752 0.7796 -0.9529 2.1033 0.4607
## EEAA 0.9721 0.9919 -0.9719 2.9162 0.3270
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.01
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.3988 0.6645 0.0964 2.7011 0.0353
## AgeAccelerationResidualHannum 0.1585 0.6288 -1.0739 1.3910 0.8010
## AgeAccelPheno 0.8106 0.5817 -0.3295 1.9507 0.1635
## DNAmAgeSkinBloodClockAdjAge 0.9757 0.5683 -0.1381 2.0895 0.0860
## AgeAccelGrim -0.5896 0.3312 -1.2388 0.0596 0.0751
## DNAmTLAdjAge -0.0236 0.0259 -0.0744 0.0272 0.3627
## IEAA 1.4119 0.5749 0.2850 2.5387 0.0141
## EEAA 0.4488 0.8061 -1.1312 2.0289 0.5777
## sig_level
## AgeAccelerationResidual <= 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA <= 0.05
## EEAA > 0.05
##
## $CUM6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.4727 0.4502 -1.3550 0.4096 0.2937
## AgeAccelerationResidualHannum 0.1441 0.4278 -0.6943 0.9826 0.7361
## AgeAccelPheno -0.4277 0.4670 -1.3430 0.4877 0.3598
## DNAmAgeSkinBloodClockAdjAge -0.5165 0.4695 -1.4368 0.4037 0.2713
## AgeAccelGrim 0.5851 0.2662 0.0633 1.1068 0.0280
## DNAmTLAdjAge -0.0370 0.0223 -0.0806 0.0066 0.0962
## IEAA -0.7212 0.3899 -1.4854 0.0429 0.0643
## EEAA 0.1993 0.5869 -0.9511 1.3498 0.7341
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4199 0.7076 -0.9670 1.8067 0.5529
## AgeAccelerationResidualHannum -0.7094 0.6050 -1.8953 0.4764 0.2410
## AgeAccelPheno -0.6785 0.6227 -1.8991 0.5420 0.2759
## DNAmAgeSkinBloodClockAdjAge -0.4159 0.5630 -1.5194 0.6876 0.4601
## AgeAccelGrim -0.0469 0.4041 -0.8389 0.7450 0.9075
## DNAmTLAdjAge 0.0255 0.0262 -0.0259 0.0770 0.3309
## IEAA 0.8649 0.5691 -0.2505 1.9803 0.1286
## EEAA -0.8810 0.7723 -2.3947 0.6327 0.2540
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.3489 0.6759 -1.6737 0.9759 0.6057
## AgeAccelerationResidualHannum -0.1683 0.6773 -1.4958 1.1592 0.8038
## AgeAccelPheno 0.0488 0.5180 -0.9665 1.0641 0.9249
## DNAmAgeSkinBloodClockAdjAge -0.2951 0.5765 -1.4250 0.8347 0.6087
## AgeAccelGrim -0.1468 0.2896 -0.7144 0.4209 0.6124
## DNAmTLAdjAge 0.0337 0.0240 -0.0135 0.0808 0.1614
## IEAA -0.1429 0.5625 -1.2455 0.9597 0.7994
## EEAA -0.5937 0.8232 -2.2073 1.0198 0.4708
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
Likelihood ratio (LR) test (no confounders)
Full model: \[Y = \beta_0 + \beta_1 *
\text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} +
\beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} +
\epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.5551 0.7401
## Hannum EAA 0.8488 0.8488
## PhenoAge EAA 0.1559 0.4157
## Skin&Blood EAA 0.2862 0.5202
## GrimAge EAA 0.0170 0.1360
## DNAmTL 0.1043 0.4157
## IEAA 0.3251 0.5202
## EEAA 0.7581 0.8488
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[Y = \beta_0 + \beta_1 *
\text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} +
\beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} +
\epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.6381 0.6381
## Hannum EAA 0.5536 0.6342
## PhenoAge EAA 0.0248 0.1984
## Skin&Blood EAA 0.1313 0.2626
## GrimAge EAA 0.1039 0.2626
## DNAmTL 0.0790 0.2626
## IEAA 0.4141 0.6342
## EEAA 0.5549 0.6342
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[Y = \beta_0 + \beta_1 *
\text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} +
\beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} +
\epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.6380 0.6380
## Hannum EAA 0.3701 0.4914
## PhenoAge EAA 0.0243 0.1944
## Skin&Blood EAA 0.0878 0.2054
## GrimAge EAA 0.1027 0.2054
## DNAmTL 0.0826 0.2054
## IEAA 0.4300 0.4914
## EEAA 0.3562 0.4914
GEE (no confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $CUM6_BC_NO2_PM
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1734 0.4456 -1.0467 0.6999 0.6971
## AgeAccelerationResidualHannum 0.4721 0.3234 -0.1618 1.1060 0.1444
## AgeAccelPheno 0.3496 0.4102 -0.4544 1.1536 0.3941
## DNAmAgeSkinBloodClockAdjAge -0.0335 0.3769 -0.7723 0.7052 0.9291
## AgeAccelGrim 0.4367 0.2690 -0.0905 0.9639 0.1045
## DNAmTLAdjAge -0.0209 0.0177 -0.0557 0.0138 0.2375
## IEAA 0.1711 0.4039 -0.6205 0.9627 0.6718
## EEAA 0.5373 0.4342 -0.3137 1.3882 0.2159
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_PAH36
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1126 0.4644 -1.0228 0.7975 0.8084
## AgeAccelerationResidualHannum 0.5499 0.3995 -0.2330 1.3329 0.1686
## AgeAccelPheno 0.6414 0.3990 -0.1406 1.4235 0.1079
## DNAmAgeSkinBloodClockAdjAge 0.4022 0.3552 -0.2940 1.0983 0.2575
## AgeAccelGrim 0.7544 0.2700 0.2252 1.2837 0.0052
## DNAmTLAdjAge -0.0101 0.0178 -0.0450 0.0248 0.5721
## IEAA -0.0190 0.4417 -0.8847 0.8468 0.9658
## EEAA 0.6339 0.4885 -0.3236 1.5914 0.1944
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4152 0.4627 -0.4917 1.3221 0.3695
## AgeAccelerationResidualHannum 0.1178 0.4132 -0.6920 0.9277 0.7755
## AgeAccelPheno 0.4277 0.3622 -0.2821 1.1376 0.2376
## DNAmAgeSkinBloodClockAdjAge 0.1169 0.3244 -0.5188 0.7527 0.7184
## AgeAccelGrim -0.1473 0.2265 -0.5913 0.2968 0.5157
## DNAmTLAdjAge -0.0365 0.0141 -0.0641 -0.0089 0.0096
## IEAA 0.5897 0.4160 -0.2256 1.4051 0.1563
## EEAA 0.1673 0.5172 -0.8464 1.1810 0.7463
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge <= 0.01
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1120 0.3737 -0.6205 0.8445 0.7644
## AgeAccelerationResidualHannum 0.2033 0.3729 -0.5276 0.9342 0.5856
## AgeAccelPheno 0.0495 0.3930 -0.7208 0.8197 0.8999
## DNAmAgeSkinBloodClockAdjAge -0.1305 0.3274 -0.7723 0.5112 0.6901
## AgeAccelGrim 0.6356 0.2405 0.1642 1.1070 0.0082
## DNAmTLAdjAge -0.0408 0.0151 -0.0704 -0.0112 0.0069
## IEAA 0.1163 0.3180 -0.5070 0.7395 0.7147
## EEAA 0.2987 0.4929 -0.6673 1.2647 0.5445
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge <= 0.01
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0812 0.5705 -1.0370 1.1995 0.8868
## AgeAccelerationResidualHannum -0.0428 0.4251 -0.8759 0.7903 0.9198
## AgeAccelPheno -0.3113 0.4603 -1.2136 0.5910 0.4989
## DNAmAgeSkinBloodClockAdjAge -0.3598 0.4878 -1.3160 0.5964 0.4608
## AgeAccelGrim 0.7351 0.3803 -0.0103 1.4805 0.0532
## DNAmTLAdjAge -0.0127 0.0207 -0.0533 0.0279 0.5400
## IEAA 0.4026 0.4841 -0.5463 1.3516 0.4056
## EEAA -0.0053 0.5756 -1.1335 1.1230 0.9927
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $CUM6_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.5240 0.5532 -1.6082 0.5602 0.3435
## AgeAccelerationResidualHannum 0.0312 0.4619 -0.8742 0.9366 0.9461
## AgeAccelPheno 0.4436 0.4352 -0.4094 1.2965 0.3081
## DNAmAgeSkinBloodClockAdjAge -0.0997 0.4221 -0.9269 0.7276 0.8133
## AgeAccelGrim -0.3178 0.2644 -0.8360 0.2004 0.2293
## DNAmTLAdjAge 0.0085 0.0193 -0.0294 0.0463 0.6616
## IEAA -0.1665 0.4981 -1.1428 0.8097 0.7381
## EEAA -0.3398 0.5481 -1.4140 0.7345 0.5353
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
3.4. Clusters based on pollutant measurements (clusMEAS6)
The file “LEX_clusMEAS6.csv” has information on measured pollutant
exposures during each visit. Estimates are available for 6 different
prototypes (cluster variables) for a total of 54 subjects and 54 visits.
The prototypes are labelled as:
MEAS6_BC_ PM_RET – a cluster of BC, PM, and retene
MEAS6_X31 – a large cluster of 31 air pollutants
MEAS6_X5 – a smaller cluster of 5 air pollutants
MEAS6_DlP – DlP only
MEAS6_NkF – NkF only
MEAS6_ NO2_SO2 – NO2, and SO2
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| MEAS6_BC_PM_RET |
0.05 (-0.6, 0.5) |
-0.40 (-1.6, -0.3) |
0.07 (-0.5, 0.5) |
1.08 (0.5, 2.1) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_X31 |
0.19 (-0.6, 0.7) |
-1.02 (-1.8, -0.8) |
0.31 (-0.1, 0.8) |
0.35 (-0.5, 0.8) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_X5 |
-0.14 (-1.0, 1.0) |
-1.07 (-1.1, -1.0) |
0.46 (-0.8, 1.1) |
0.55 (-0.1, 0.9) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_DlP |
-0.63 (-0.7, 1.3) |
0.35 (-0.6, 1.0) |
-0.69 (-0.7, 1.2) |
-0.30 (-0.5, 1.3) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_NkF |
-0.50 (-0.6, 1.2) |
-0.39 (-0.6, 0.6) |
-0.50 (-0.6, 1.2) |
-0.50 (-0.7, 0.2) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_NO2_SO2 |
-0.08 (-0.9, 0.8) |
0.98 (0.5, 1.5) |
-0.37 (-0.9, 0.8) |
-0.37 (-1.3, 0.2) |
| (Missing) |
70 |
10 |
57 |
3 |
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{BC_PM_RET} + \beta_2 * \text{X31}
+ \beta_3 * \text{X5} + \beta_4 * \text{DlP} + \beta_5 * \text{NkF} +
\beta_6 * \text{NO2_SO2}\\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10}
* edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0638 0.1542
## Hannum EAA 0.0771 0.1542
## PhenoAge EAA 0.1075 0.1720
## Skin&Blood EAA 0.0064 0.0512
## GrimAge EAA 0.2722 0.3111
## DNAmTL 0.4302 0.4302
## IEAA 0.2396 0.3111
## EEAA 0.0473 0.1542
GEE (Mix)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $MEAS6_BC_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2296 0.7304 -1.6613 1.2020 0.7533
## AgeAccelerationResidualHannum -0.3400 0.5285 -1.3759 0.6958 0.5200
## AgeAccelPheno -0.3903 0.7498 -1.8598 1.0792 0.6027
## DNAmAgeSkinBloodClockAdjAge 0.0186 0.5444 -1.0484 1.0857 0.9727
## AgeAccelGrim 0.9907 0.5954 -0.1763 2.1577 0.0961
## DNAmTLAdjAge 0.0158 0.0363 -0.0554 0.0869 0.6638
## IEAA -0.2816 0.5814 -1.4212 0.8580 0.6282
## EEAA -0.6547 0.7138 -2.0538 0.7444 0.3590
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.4360 0.6955 0.0728 2.7992 0.0389
## AgeAccelerationResidualHannum 1.1210 0.6111 -0.0767 2.3187 0.0666
## AgeAccelPheno 1.1073 0.7156 -0.2953 2.5099 0.1218
## DNAmAgeSkinBloodClockAdjAge 1.4220 0.5950 0.2558 2.5882 0.0169
## AgeAccelGrim 0.9940 0.3785 0.2522 1.7358 0.0086
## DNAmTLAdjAge -0.0234 0.0252 -0.0727 0.0260 0.3530
## IEAA 0.7468 0.5617 -0.3542 1.8478 0.1837
## EEAA 1.1480 0.6803 -0.1853 2.4813 0.0915
## sig_level
## AgeAccelerationResidual <= 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X5
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0783 0.8406 -1.5693 1.7260 0.9257
## AgeAccelerationResidualHannum -0.3408 0.7930 -1.8950 1.2135 0.6674
## AgeAccelPheno 0.1365 0.7924 -1.4166 1.6897 0.8632
## DNAmAgeSkinBloodClockAdjAge 0.9817 0.6831 -0.3571 2.3206 0.1507
## AgeAccelGrim 0.7447 0.4516 -0.1404 1.6299 0.0991
## DNAmTLAdjAge 0.0274 0.0307 -0.0328 0.0876 0.3717
## IEAA 0.2885 0.7009 -1.0854 1.6623 0.6806
## EEAA -0.9744 0.9620 -2.8599 0.9112 0.3111
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.2163 0.8624 -1.4740 1.9066 0.8019
## AgeAccelerationResidualHannum -0.0537 0.7353 -1.4948 1.3875 0.9418
## AgeAccelPheno 0.8796 0.7762 -0.6418 2.4010 0.2571
## DNAmAgeSkinBloodClockAdjAge -1.2744 0.6524 -2.5531 0.0044 0.0508
## AgeAccelGrim -0.0727 0.5791 -1.2078 1.0623 0.9001
## DNAmTLAdjAge -0.0217 0.0336 -0.0876 0.0441 0.5176
## IEAA 0.3652 0.6620 -0.9323 1.6626 0.5812
## EEAA -0.1406 0.9467 -1.9961 1.7150 0.8820
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0266 0.7390 -1.4219 1.4751 0.9713
## AgeAccelerationResidualHannum 0.5171 0.6153 -0.6888 1.7230 0.4006
## AgeAccelPheno -0.4109 0.6884 -1.7603 0.9384 0.5506
## DNAmAgeSkinBloodClockAdjAge 0.1726 0.5861 -0.9760 1.3213 0.7683
## AgeAccelGrim -0.2478 0.4258 -1.0825 0.5868 0.5606
## DNAmTLAdjAge -0.0070 0.0298 -0.0654 0.0513 0.8132
## IEAA -0.3908 0.6749 -1.7137 0.9321 0.5626
## EEAA 0.8254 0.8254 -0.7923 2.4432 0.3173
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NO2_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.8202 0.7289 -2.2488 0.6085 0.2605
## AgeAccelerationResidualHannum 0.5337 0.5810 -0.6052 1.6725 0.3584
## AgeAccelPheno 0.7834 0.6617 -0.5134 2.0803 0.2364
## DNAmAgeSkinBloodClockAdjAge -0.2823 0.5153 -1.2923 0.7276 0.5837
## AgeAccelGrim -0.0630 0.4900 -1.0233 0.8973 0.8977
## DNAmTLAdjAge -0.0436 0.0339 -0.1100 0.0229 0.1986
## IEAA -0.9758 0.5843 -2.1209 0.1694 0.0949
## EEAA 0.2386 0.7346 -1.2013 1.6784 0.7454
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (Mix, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * BC\_PAH6 + \beta_2 PAH31 + \beta_3 NkF
+ \beta_4 PM\_RET + \beta_5 NO2 + \beta_6 SO2 \\
& + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_10 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## The estimated effects:
## $MEAS6_BC_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.6878 1.0912 -3.8266 0.4511 0.1219
## AgeAccelerationResidualHannum -1.1844 0.8687 -2.8871 0.5183 0.1728
## AgeAccelPheno -1.2972 0.9505 -3.1601 0.5656 0.1723
## DNAmAgeSkinBloodClockAdjAge -1.8335 0.8283 -3.4570 -0.2100 0.0269
## AgeAccelGrim 0.5460 0.8198 -1.0609 2.1528 0.5055
## DNAmTLAdjAge 0.0185 0.0483 -0.0763 0.1132 0.7024
## IEAA -1.3573 0.8971 -3.1156 0.4011 0.1303
## EEAA -1.4932 1.0864 -3.6225 0.6362 0.1693
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 3.5341 1.1395 1.3006 5.7676 0.0019
## AgeAccelerationResidualHannum 2.8712 0.7538 1.3938 4.3486 0.0001
## AgeAccelPheno 2.5713 0.9291 0.7503 4.3924 0.0056
## DNAmAgeSkinBloodClockAdjAge 2.8039 0.7099 1.4124 4.1954 0.0001
## AgeAccelGrim 1.1723 0.6026 -0.0087 2.3534 0.0517
## DNAmTLAdjAge -0.0770 0.0383 -0.1522 -0.0019 0.0446
## IEAA 1.9296 1.0077 -0.0455 3.9046 0.0555
## EEAA 3.7235 0.9840 1.7948 5.6521 0.0002
## sig_level
## AgeAccelerationResidual <= 0.01
## AgeAccelerationResidualHannum <= 0.001
## AgeAccelPheno <= 0.01
## DNAmAgeSkinBloodClockAdjAge <= 0.001
## AgeAccelGrim > 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA <= 0.001
##
## $MEAS6_X5
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.7383 1.3477 -4.3798 0.9033 0.1971
## AgeAccelerationResidualHannum -1.7848 1.2037 -4.1439 0.5744 0.1381
## AgeAccelPheno -1.1187 1.0756 -3.2268 0.9894 0.2983
## DNAmAgeSkinBloodClockAdjAge -0.2191 0.8492 -1.8834 1.4453 0.7964
## AgeAccelGrim -0.5506 0.9704 -2.4526 1.3515 0.5705
## DNAmTLAdjAge 0.0735 0.0505 -0.0255 0.1724 0.1454
## IEAA -0.5405 1.1742 -2.8419 1.7610 0.6453
## EEAA -2.8972 1.4724 -5.7831 -0.0114 0.0491
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA <= 0.05
##
## $MEAS6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.5500 0.7252 -1.9715 0.8714 0.4482
## AgeAccelerationResidualHannum -0.7140 0.5672 -1.8258 0.3978 0.2081
## AgeAccelPheno 0.3237 0.6517 -0.9537 1.6011 0.6194
## DNAmAgeSkinBloodClockAdjAge -1.7802 0.5331 -2.8250 -0.7354 0.0008
## AgeAccelGrim -0.2730 0.4581 -1.1709 0.6248 0.5512
## DNAmTLAdjAge -0.0016 0.0274 -0.0554 0.0521 0.9526
## IEAA -0.0277 0.6383 -1.2787 1.2234 0.9654
## EEAA -1.0554 0.6892 -2.4062 0.2953 0.1257
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.001
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.1598 0.8232 -2.7733 0.4537 0.1589
## AgeAccelerationResidualHannum -0.3325 0.6884 -1.6818 1.0168 0.6291
## AgeAccelPheno -1.0329 0.6362 -2.2799 0.2141 0.1045
## DNAmAgeSkinBloodClockAdjAge -0.4344 0.5534 -1.5192 0.6504 0.4325
## AgeAccelGrim -0.6398 0.5874 -1.7911 0.5115 0.2761
## DNAmTLAdjAge 0.0175 0.0320 -0.0453 0.0802 0.5854
## IEAA -1.0295 0.8361 -2.6682 0.6091 0.2182
## EEAA -0.4770 0.8629 -2.1682 1.2143 0.5804
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NO2_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -1.1647 0.6790 -2.4956 0.1662 0.0863
## AgeAccelerationResidualHannum 0.4105 0.5214 -0.6115 1.4326 0.4311
## AgeAccelPheno 0.4768 0.6388 -0.7753 1.7288 0.4555
## DNAmAgeSkinBloodClockAdjAge -0.5205 0.5551 -1.6084 0.5674 0.3484
## AgeAccelGrim -0.0731 0.4530 -0.9609 0.8147 0.8718
## DNAmTLAdjAge -0.0404 0.0354 -0.1097 0.0290 0.2542
## IEAA -1.3120 0.5558 -2.4013 -0.2227 0.0182
## EEAA 0.0801 0.6184 -1.1320 1.2922 0.8970
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA <= 0.05
## EEAA > 0.05
Sensitivity analyses
Likelihood ratio (LR) test (no confounders)
Full model: \[Y = \beta_0 + \beta_1 *
\text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4
* \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2} +
\epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.1034 0.1654
## Hannum EAA 0.0550 0.1306
## PhenoAge EAA 0.0653 0.1306
## Skin&Blood EAA 0.0353 0.1306
## GrimAge EAA 0.1928 0.2571
## DNAmTL 0.2487 0.2842
## IEAA 0.5142 0.5142
## EEAA 0.0263 0.1306
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[Y = \beta_0 + \beta_1 *
\text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4
* \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} +
\epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0715 0.1834
## Hannum EAA 0.1314 0.2102
## PhenoAge EAA 0.2403 0.3204
## Skin&Blood EAA 0.0446 0.1834
## GrimAge EAA 0.0917 0.1834
## DNAmTL 0.4322 0.4322
## IEAA 0.3579 0.4090
## EEAA 0.0624 0.1834
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[Y = \beta_0 + \beta_1 *
\text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4
* \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2} +
\epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.1992 0.3187
## Hannum EAA 0.1754 0.3187
## PhenoAge EAA 0.2713 0.3617
## Skin&Blood EAA 0.0873 0.2472
## GrimAge EAA 0.0319 0.2472
## DNAmTL 0.4242 0.4848
## IEAA 0.6646 0.6646
## EEAA 0.0927 0.2472
GEE (no confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $MEAS6_BC_PM_RET
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2510 0.6406 -1.5065 1.0046 0.6952
## AgeAccelerationResidualHannum -0.3925 0.4998 -1.3721 0.5871 0.4323
## AgeAccelPheno -0.3475 0.6677 -1.6561 0.9611 0.6028
## DNAmAgeSkinBloodClockAdjAge 0.0746 0.4844 -0.8749 1.0241 0.8776
## AgeAccelGrim 1.0200 0.6097 -0.1749 2.2149 0.0943
## DNAmTLAdjAge 0.0175 0.0357 -0.0525 0.0875 0.6247
## IEAA -0.1676 0.5048 -1.1571 0.8218 0.7398
## EEAA -0.7563 0.6989 -2.1262 0.6136 0.2792
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X31
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 1.2983 0.7075 -0.0883 2.6850 0.0665
## AgeAccelerationResidualHannum 0.9943 0.6643 -0.3077 2.2963 0.1344
## AgeAccelPheno 1.0479 0.7016 -0.3273 2.4231 0.1353
## DNAmAgeSkinBloodClockAdjAge 1.3015 0.5981 0.1293 2.4738 0.0295
## AgeAccelGrim 1.0821 0.3898 0.3182 1.8461 0.0055
## DNAmTLAdjAge -0.0229 0.0270 -0.0757 0.0300 0.3962
## IEAA 0.6828 0.5403 -0.3761 1.7418 0.2063
## EEAA 1.0135 0.7790 -0.5133 2.5403 0.1932
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim <= 0.01
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_X5
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1795 0.7126 -1.5762 1.2171 0.8011
## AgeAccelerationResidualHannum -0.5288 0.6335 -1.7704 0.7129 0.4039
## AgeAccelPheno -0.1223 0.6350 -1.3669 1.1222 0.8472
## DNAmAgeSkinBloodClockAdjAge 0.6389 0.5676 -0.4737 1.7514 0.2604
## AgeAccelGrim 0.5114 0.3940 -0.2609 1.2836 0.1943
## DNAmTLAdjAge 0.0366 0.0253 -0.0130 0.0863 0.1481
## IEAA 0.2756 0.5588 -0.8196 1.3708 0.6218
## EEAA -1.1792 0.7735 -2.6953 0.3370 0.1274
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_DlP
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3500 0.7824 -1.1834 1.8835 0.6546
## AgeAccelerationResidualHannum 0.0773 0.6774 -1.2503 1.4049 0.9091
## AgeAccelPheno 0.8949 0.6821 -0.4421 2.2319 0.1895
## DNAmAgeSkinBloodClockAdjAge -1.0079 0.6161 -2.2153 0.1996 0.1018
## AgeAccelGrim -0.0330 0.6191 -1.2464 1.1803 0.9574
## DNAmTLAdjAge -0.0260 0.0316 -0.0880 0.0360 0.4108
## IEAA 0.3756 0.6135 -0.8270 1.5781 0.5405
## EEAA 0.0587 0.8749 -1.6562 1.7735 0.9465
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NkF
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1813 0.7858 -1.3589 1.7216 0.8175
## AgeAccelerationResidualHannum 0.6335 0.6217 -0.5850 1.8519 0.3082
## AgeAccelPheno -0.2041 0.6978 -1.5718 1.1636 0.7699
## DNAmAgeSkinBloodClockAdjAge 0.2485 0.6376 -1.0012 1.4982 0.6967
## AgeAccelGrim -0.0278 0.4657 -0.9405 0.8850 0.9525
## DNAmTLAdjAge -0.0165 0.0281 -0.0715 0.0385 0.5558
## IEAA -0.3822 0.6852 -1.7253 0.9608 0.5770
## EEAA 1.0090 0.8230 -0.6042 2.6222 0.2202
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $MEAS6_NO2_SO2
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2626 0.7499 -1.7325 1.2072 0.7262
## AgeAccelerationResidualHannum 0.5492 0.6170 -0.6601 1.7585 0.3734
## AgeAccelPheno 0.8929 0.6070 -0.2968 2.0826 0.1413
## DNAmAgeSkinBloodClockAdjAge 0.1422 0.6079 -1.0494 1.3337 0.8151
## AgeAccelGrim 0.3458 0.4086 -0.4552 1.1467 0.3975
## DNAmTLAdjAge -0.0429 0.0284 -0.0985 0.0127 0.1308
## IEAA -0.4182 0.6336 -1.6601 0.8237 0.5093
## EEAA 0.3235 0.7842 -1.2135 1.8604 0.6800
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
3.5. Clusters based on urinary biomarkers (clusURI5)
The file “LEX_clusURI5.csv” has information on measured urinary
biomarkers obtained during each visit. Estimates are available for 5
different prototypes (cluster variables) for a total of 163 subjects and
186 visits. The prototypes are labelled as:
URI5_NAP_1M_2M – a cluster of Naphthalene, 1Methylnaphthalene, and
2Methylnaphthalene
URI5_ACE – Acenaphthene only
URI5_FLU_PHE – Fluoranthene and Phenanthrene_anth
URI5_PYR – Pyrene only
URI5_CHR – Baa_Chrysene only
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| MEAS6_BC_PM_RET |
0.05 (-0.6, 0.5) |
-0.40 (-1.6, -0.3) |
0.07 (-0.5, 0.5) |
1.08 (0.5, 2.1) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_X31 |
0.19 (-0.6, 0.7) |
-1.02 (-1.8, -0.8) |
0.31 (-0.1, 0.8) |
0.35 (-0.5, 0.8) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_X5 |
-0.14 (-1.0, 1.0) |
-1.07 (-1.1, -1.0) |
0.46 (-0.8, 1.1) |
0.55 (-0.1, 0.9) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_DlP |
-0.63 (-0.7, 1.3) |
0.35 (-0.6, 1.0) |
-0.69 (-0.7, 1.2) |
-0.30 (-0.5, 1.3) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_NkF |
-0.50 (-0.6, 1.2) |
-0.39 (-0.6, 0.6) |
-0.50 (-0.6, 1.2) |
-0.50 (-0.7, 0.2) |
| (Missing) |
70 |
10 |
57 |
3 |
| MEAS6_NO2_SO2 |
-0.08 (-0.9, 0.8) |
0.98 (0.5, 1.5) |
-0.37 (-0.9, 0.8) |
-0.37 (-1.3, 0.2) |
| (Missing) |
70 |
10 |
57 |
3 |
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE}
+ \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 *
\text{CHR}\\
& + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} *
edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.7325 0.8371
## Hannum EAA 0.5927 0.8371
## PhenoAge EAA 0.0185 0.1480
## Skin&Blood EAA 0.4011 0.8022
## GrimAge EAA 0.0676 0.2704
## DNAmTL 0.1731 0.4616
## IEAA 0.8470 0.8470
## EEAA 0.6407 0.8371
GEE (Mix)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X \\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $URI5_NAP_1M_2M
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1520 0.5218 -0.8708 1.1748 0.7708
## AgeAccelerationResidualHannum -0.4606 0.4434 -1.3296 0.4084 0.2989
## AgeAccelPheno -0.0180 0.4653 -0.9300 0.8940 0.9691
## DNAmAgeSkinBloodClockAdjAge -0.2757 0.3827 -1.0259 0.4744 0.4712
## AgeAccelGrim -0.2704 0.4773 -1.2059 0.6652 0.5711
## DNAmTLAdjAge -0.0180 0.0351 -0.0868 0.0509 0.6090
## IEAA 0.0067 0.5552 -1.0815 1.0949 0.9904
## EEAA -0.0176 0.5402 -1.0764 1.0411 0.9740
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_ACE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0005 0.4278 -0.8390 0.8381 0.9991
## AgeAccelerationResidualHannum 0.4834 0.3333 -0.1699 1.1367 0.1470
## AgeAccelPheno 0.8926 0.4735 -0.0355 1.8206 0.0594
## DNAmAgeSkinBloodClockAdjAge -0.0704 0.3143 -0.6864 0.5456 0.8228
## AgeAccelGrim -1.2920 0.3333 -1.9453 -0.6387 0.0001
## DNAmTLAdjAge 0.0026 0.0332 -0.0626 0.0677 0.9379
## IEAA 0.3215 0.4291 -0.5196 1.1626 0.4537
## EEAA 0.6255 0.4581 -0.2725 1.5234 0.1722
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim <= 0.001
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_FLU_PHE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.3410 0.4843 -0.6081 1.2902 0.4813
## AgeAccelerationResidualHannum 0.1956 0.4495 -0.6853 1.0766 0.6634
## AgeAccelPheno 0.4084 0.4202 -0.4152 1.2320 0.3311
## DNAmAgeSkinBloodClockAdjAge 0.0197 0.3263 -0.6199 0.6593 0.9519
## AgeAccelGrim 1.0882 1.6256 -2.0980 4.2743 0.5032
## DNAmTLAdjAge -0.0235 0.0691 -0.1588 0.1119 0.7340
## IEAA 0.2347 0.4903 -0.7263 1.1956 0.6322
## EEAA 0.5243 0.5941 -0.6402 1.6888 0.3775
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_PYR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0587 0.4785 -0.8790 0.9965 0.9023
## AgeAccelerationResidualHannum 0.3959 0.4305 -0.4478 1.2396 0.3577
## AgeAccelPheno 1.1174 0.4847 0.1674 2.0673 0.0211
## DNAmAgeSkinBloodClockAdjAge 0.6638 0.4167 -0.1528 1.4805 0.1111
## AgeAccelGrim -0.1384 1.5144 -3.1065 2.8298 0.9272
## DNAmTLAdjAge -0.0365 0.0323 -0.0997 0.0267 0.2577
## IEAA -0.0151 0.4872 -0.9700 0.9398 0.9753
## EEAA 0.6232 0.5492 -0.4533 1.6997 0.2565
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_CHR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1114 0.3838 -0.6408 0.8637 0.7716
## AgeAccelerationResidualHannum 0.0802 0.4224 -0.7476 0.9080 0.8494
## AgeAccelPheno 0.0207 0.3799 -0.7239 0.7653 0.9566
## DNAmAgeSkinBloodClockAdjAge 0.0762 0.3130 -0.5373 0.6897 0.8077
## AgeAccelGrim 0.1398 0.7358 -1.3023 1.5820 0.8493
## DNAmTLAdjAge 0.0178 0.3096 -0.5891 0.6247 0.9542
## IEAA -0.1849 0.4274 -1.0225 0.6528 0.6653
## EEAA 0.2934 0.5105 -0.7071 1.2939 0.5654
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
GEE (Mix, mutual adjust)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * NAP\_1M\_2M + \beta_2 ACE + \beta_3
FLU\_PHE + \beta_4 PYR + \beta_5 CHR \\
& + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_9 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## The estimated effects:
## $URI5_NAP_1M_2M
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0772 0.6297 -1.3115 1.1571 0.9024
## AgeAccelerationResidualHannum -0.9973 0.5285 -2.0332 0.0385 0.0591
## AgeAccelPheno -0.4612 0.6006 -1.6384 0.7161 0.4426
## DNAmAgeSkinBloodClockAdjAge -0.4410 0.5162 -1.4528 0.5707 0.3929
## AgeAccelGrim 0.0385 1.3493 -2.6061 2.6831 0.9773
## DNAmTLAdjAge -0.0117 0.1841 -0.3726 0.3492 0.9492
## IEAA -0.3117 0.6536 -1.5928 0.9694 0.6334
## EEAA -0.6005 0.6512 -1.8768 0.6758 0.3564
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_ACE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0026 0.4246 -0.8348 0.8297 0.9952
## AgeAccelerationResidualHannum 0.5312 0.3182 -0.0925 1.1548 0.0950
## AgeAccelPheno 0.9146 0.4577 0.0175 1.8118 0.0457
## DNAmAgeSkinBloodClockAdjAge -0.0633 0.2924 -0.6364 0.5097 0.8285
## AgeAccelGrim 0.3306 3.8727 -7.2599 7.9212 0.9320
## DNAmTLAdjAge -0.0277 0.0504 -0.1265 0.0712 0.5833
## IEAA 0.2828 0.4335 -0.5668 1.1325 0.5141
## EEAA 0.6471 0.4666 -0.2675 1.5616 0.1655
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_FLU_PHE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.4942 0.7136 -0.9044 1.8928 0.4886
## AgeAccelerationResidualHannum 0.8156 0.5959 -0.3524 1.9835 0.1711
## AgeAccelPheno 0.0888 0.6202 -1.1268 1.3044 0.8862
## DNAmAgeSkinBloodClockAdjAge -0.1798 0.4682 -1.0975 0.7379 0.7009
## AgeAccelGrim 0.3924 3.4441 -6.3580 7.1428 0.9093
## DNAmTLAdjAge -0.0361 0.2106 -0.4488 0.3767 0.8639
## IEAA 0.7557 0.6585 -0.5350 2.0464 0.2512
## EEAA 0.6230 0.7469 -0.8408 2.0869 0.4042
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_PYR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.2034 0.5618 -1.3044 0.8977 0.7174
## AgeAccelerationResidualHannum 0.2590 0.3895 -0.5044 1.0223 0.5061
## AgeAccelPheno 1.2211 0.5215 0.1989 2.2432 0.0192
## DNAmAgeSkinBloodClockAdjAge 0.9278 0.4547 0.0366 1.8189 0.0413
## AgeAccelGrim 0.2333 1.2996 -2.3139 2.7805 0.8575
## DNAmTLAdjAge 0.0256 0.0751 -0.1216 0.1728 0.7331
## IEAA -0.2820 0.6001 -1.4582 0.8941 0.6384
## EEAA 0.4017 0.5142 -0.6061 1.4096 0.4346
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge <= 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_CHR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.0725 0.4479 -0.9504 0.8054 0.8714
## AgeAccelerationResidualHannum 0.0483 0.4376 -0.8094 0.9061 0.9120
## AgeAccelPheno -0.0112 0.3626 -0.7219 0.6996 0.9754
## DNAmAgeSkinBloodClockAdjAge 0.1188 0.3586 -0.5841 0.8217 0.7405
## AgeAccelGrim 0.2797 0.4074 -0.5189 1.0783 0.4924
## DNAmTLAdjAge 0.0028 0.0273 -0.0507 0.0564 0.9170
## IEAA -0.3520 0.4199 -1.1751 0.4711 0.4019
## EEAA 0.1725 0.5044 -0.8162 1.1612 0.7323
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
Sensitivity analyses
Likelihood ratio (LR) test (no confounders)
Full model: \[Y = \beta_0 + \beta_1 *
\text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} +
\beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.7166 0.9146
## Hannum EAA 0.8605 0.9146
## PhenoAge EAA 0.0779 0.5412
## Skin&Blood EAA 0.5460 0.9146
## GrimAge EAA 0.2178 0.5808
## DNAmTL 0.1353 0.5412
## IEAA 0.7881 0.9146
## EEAA 0.9146 0.9146
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[Y = \beta_0 + \beta_1 *
\text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} +
\beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.5630 0.9535
## Hannum EAA 0.7871 0.9535
## PhenoAge EAA 0.1480 0.9535
## Skin&Blood EAA 0.9124 0.9535
## GrimAge EAA 0.8240 0.9535
## DNAmTL 0.5162 0.9535
## IEAA 0.7267 0.9535
## EEAA 0.9535 0.9535
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[Y = \beta_0 + \beta_1 *
\text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} +
\beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.7266 0.9357
## Hannum EAA 0.8797 0.9357
## PhenoAge EAA 0.1130 0.9040
## Skin&Blood EAA 0.6715 0.9357
## GrimAge EAA 0.8736 0.9357
## DNAmTL 0.6495 0.9357
## IEAA 0.8091 0.9357
## EEAA 0.9357 0.9357
GEE (no confounders)
In this section, we perform the generalized estimating equations
(GEE) to evaluate the associations between each cluster within current
pollutant exposures and each Epigenetic Age Acceleration with the
formula: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations and X is one of the cluster estimates.
Results:
## The estimated effects:
## $URI5_NAP_1M_2M
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.1451 0.4798 -0.7953 1.0855 0.7624
## AgeAccelerationResidualHannum -0.4140 0.4297 -1.2562 0.4282 0.3353
## AgeAccelPheno -0.0402 0.4520 -0.9261 0.8457 0.9292
## DNAmAgeSkinBloodClockAdjAge -0.2916 0.3794 -1.0352 0.4521 0.4422
## AgeAccelGrim 0.7905 0.7292 -0.6387 2.2197 0.2783
## DNAmTLAdjAge -0.0211 0.0165 -0.0535 0.0112 0.2007
## IEAA -0.0220 0.5284 -1.0576 1.0136 0.9668
## EEAA -0.0926 0.5295 -1.1303 0.9452 0.8612
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_ACE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.1857 0.4469 -1.0617 0.6902 0.6777
## AgeAccelerationResidualHannum 0.4262 0.3152 -0.1917 1.0440 0.1764
## AgeAccelPheno 0.7347 0.4369 -0.1215 1.5909 0.0926
## DNAmAgeSkinBloodClockAdjAge -0.0918 0.3105 -0.7003 0.5168 0.7676
## AgeAccelGrim 0.9474 0.7988 -0.6183 2.5131 0.2356
## DNAmTLAdjAge -0.0106 0.0124 -0.0349 0.0137 0.3914
## IEAA 0.1276 0.4252 -0.7057 0.9609 0.7641
## EEAA 0.5001 0.4234 -0.3297 1.3299 0.2375
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_FLU_PHE
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0150 0.4508 -0.8685 0.8985 0.9734
## AgeAccelerationResidualHannum 0.2083 0.4207 -0.6164 1.0329 0.6206
## AgeAccelPheno 0.4695 0.4311 -0.3756 1.3145 0.2762
## DNAmAgeSkinBloodClockAdjAge -0.0025 0.3205 -0.6306 0.6257 0.9939
## AgeAccelGrim 0.7458 0.5193 -0.2720 1.7636 0.1509
## DNAmTLAdjAge -0.0383 0.0160 -0.0698 -0.0069 0.0168
## IEAA 0.1830 0.4829 -0.7636 1.1295 0.7048
## EEAA 0.4333 0.5655 -0.6750 1.5416 0.4435
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge <= 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_PYR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual -0.4791 0.4380 -1.3376 0.3794 0.2740
## AgeAccelerationResidualHannum 0.3009 0.3733 -0.4308 1.0325 0.4202
## AgeAccelPheno 1.0068 0.4239 0.1759 1.8378 0.0176
## DNAmAgeSkinBloodClockAdjAge 0.5425 0.3945 -0.2307 1.3157 0.1691
## AgeAccelGrim 0.4524 0.3209 -0.1765 1.0813 0.1585
## DNAmTLAdjAge -0.0245 0.0185 -0.0608 0.0117 0.1840
## IEAA -0.2856 0.4530 -1.1734 0.6023 0.5284
## EEAA 0.3395 0.4839 -0.6090 1.2879 0.4830
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno <= 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
##
## $URI5_CHR
## coefficient std ci_lower ci_upper p_val
## AgeAccelerationResidual 0.0122 0.3673 -0.7077 0.7321 0.9734
## AgeAccelerationResidualHannum 0.0647 0.3947 -0.7089 0.8383 0.8699
## AgeAccelPheno 0.0456 0.3893 -0.7173 0.8086 0.9067
## DNAmAgeSkinBloodClockAdjAge 0.0382 0.2859 -0.5222 0.5986 0.8938
## AgeAccelGrim 0.4298 0.2937 -0.1458 1.0054 0.1433
## DNAmTLAdjAge -0.0272 0.0223 -0.0708 0.0165 0.2226
## IEAA -0.1711 0.3912 -0.9377 0.5956 0.6619
## EEAA 0.1957 0.4975 -0.7795 1.1709 0.6941
## sig_level
## AgeAccelerationResidual > 0.05
## AgeAccelerationResidualHannum > 0.05
## AgeAccelPheno > 0.05
## DNAmAgeSkinBloodClockAdjAge > 0.05
## AgeAccelGrim > 0.05
## DNAmTLAdjAge > 0.05
## IEAA > 0.05
## EEAA > 0.05
4.1. Current exposure to 5MC
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| cur_5mc |
7.81 (5.2, 9.7) |
2.59 (2.2, 4.0) |
9.46 (5.6, 10.1) |
7.40 (7.1, 7.4) |
| (Missing) |
3 |
2 |
1 |
0 |
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{cur_5mc} \\
& + \beta_{2} * county + \beta_{3} * BMI + \beta_{4} * ses +
\beta_{5} * edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.8392 0.9109
## Hannum EAA 0.6376 0.9109
## PhenoAge EAA 0.2551 0.6803
## Skin&Blood EAA 0.1507 0.6028
## GrimAge EAA 0.0013 0.0104
## DNAmTL 0.4367 0.8734
## IEAA 0.9109 0.9109
## EEAA 0.6856 0.9109
GEE (mix)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *cur\_5mc\\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.015878690 0.123212614165765 -0.22561803
## AgeAccelerationResidualHannum -0.051171412 0.103935109316447 -0.25488423
## AgeAccelPheno 0.091359722 0.0974953712583224 -0.09973121
## DNAmAgeSkinBloodClockAdjAge 0.114010349 0.0766727133240309 -0.03626817
## AgeAccelGrim 0.167727453 0.0513461606401706 0.06708898
## DNAmTLAdjAge -0.004550865 0.00347496993471429 -0.01136181
## IEAA 0.003400224 0.13227439652008 -0.25585759
## EEAA -0.068201795 0.13857957225156 -0.33981776
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.257375413 0.897458716697316 > 0.05
## AgeAccelerationResidualHannum 0.152541402 0.622479003468804 > 0.05
## AgeAccelPheno 0.282450650 0.348723953264527 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.264288867 0.137021616586443 > 0.05
## AgeAccelGrim 0.268365928 0.00108846725724532 <= 0.01
## DNAmTLAdjAge 0.002260076 0.190326866190183 > 0.05
## IEAA 0.262658041 0.979491967938557 > 0.05
## EEAA 0.203414167 0.622614029655613 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Sensitivity analysis
GEE (no confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *cur\_5mc + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.019159838 0.119620299681397 -0.21529595
## AgeAccelerationResidualHannum -0.041572743 0.10121495306854 -0.23995405
## AgeAccelPheno 0.063948683 0.100238231551026 -0.13251825
## DNAmAgeSkinBloodClockAdjAge 0.104833952 0.075228712841312 -0.04261433
## AgeAccelGrim 0.149523037 0.0565650873465267 0.03865547
## DNAmTLAdjAge -0.003814336 0.00353079663956072 -0.01073470
## IEAA 0.001582042 0.127834093339104 -0.24897278
## EEAA -0.053365322 0.132090247562408 -0.31226221
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.253615625 0.872745484941928 > 0.05
## AgeAccelerationResidualHannum 0.156808565 0.681265269321088 > 0.05
## AgeAccelPheno 0.260415616 0.523495170158012 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.252282229 0.163457636388872 > 0.05
## AgeAccelGrim 0.260390608 0.00820827841291805 <= 0.01
## DNAmTLAdjAge 0.003106025 0.280006515297229 > 0.05
## IEAA 0.252136865 0.990125837758072 > 0.05
## EEAA 0.205531563 0.686207920603063 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Likelihood ratio (LR) test (no confounders)
Full model: \[
Y = \beta_0 + \beta_1 * cur\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.8549 0.9308
## Hannum EAA 0.6051 0.8797
## PhenoAge EAA 0.4505 0.8797
## Skin&Blood EAA 0.2021 0.8084
## GrimAge EAA 0.0079 0.0632
## DNAmTL 0.5893 0.8797
## IEAA 0.9308 0.9308
## EEAA 0.6598 0.8797
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[
Y = \beta_0 + \beta_1 * cur\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.8991 0.9505
## Hannum EAA 0.5246 0.7767
## PhenoAge EAA 0.4812 0.7767
## Skin&Blood EAA 0.2037 0.7767
## GrimAge EAA 0.0034 0.0272
## DNAmTL 0.5825 0.7767
## IEAA 0.9505 0.9505
## EEAA 0.5364 0.7767
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[
Y = \beta_0 + \beta_1 * cur\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.4786 0.7658
## Hannum EAA 0.9613 0.9613
## PhenoAge EAA 0.3232 0.6560
## Skin&Blood EAA 0.2537 0.6560
## GrimAge EAA 0.0085 0.0680
## DNAmTL 0.6601 0.8801
## IEAA 0.3280 0.6560
## EEAA 0.7723 0.8826
4.2. Cumulative exposure to 5MC
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| cum_5mc |
253.00 (157.7, 371.9) |
92.43 (82.6, 167.9) |
270.50 (179.9, 389.1) |
341.46 (228.8, 471.5) |
| (Missing) |
3 |
2 |
1 |
0 |
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{cum_5mc} \\
& + \beta_{2} * county + \beta_{3} * BMI + \beta_{4} * ses +
\beta_{5} * edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.7436 0.7436
## Hannum EAA 0.1673 0.3346
## PhenoAge EAA 0.0945 0.2520
## Skin&Blood EAA 0.2460 0.3936
## GrimAge EAA 0.0004 0.0032
## DNAmTL 0.4779 0.6372
## IEAA 0.7222 0.7436
## EEAA 0.0927 0.2520
GEE (mix)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *cum\_5mc\\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.0002634684 0.00356364056862806 -0.0067212672
## AgeAccelerationResidualHannum 0.0034941270 0.00303739789484009 -0.0024591729
## AgeAccelPheno 0.0044586214 0.00306589829668175 -0.0015505393
## DNAmAgeSkinBloodClockAdjAge 0.0025381617 0.00268283943639575 -0.0027202036
## AgeAccelGrim 0.0059797751 0.00174133579802403 0.0025667569
## DNAmTLAdjAge -0.0001321556 0.000141285261175084 -0.0004090747
## IEAA -0.0019359695 0.00335384774017 -0.0085095111
## EEAA 0.0054840443 0.00389919260451722 -0.0021583732
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.0072482039 0.941064206728588 > 0.05
## AgeAccelerationResidualHannum 0.0094474269 0.249992106478145 > 0.05
## AgeAccelPheno 0.0104677820 0.145873502832328 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.0077965270 0.344111409021191 > 0.05
## AgeAccelGrim 0.0093927932 0.000594708988064241 <= 0.001
## DNAmTLAdjAge 0.0001447635 0.349591731827019 > 0.05
## IEAA 0.0046375721 0.563778458819213 > 0.05
## EEAA 0.0131264618 0.159588653069498 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Sensitivity analysis
GEE (no confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *cum\_5mc + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual -0.0015980048 0.00286017417861232 -0.0072039462
## AgeAccelerationResidualHannum 0.0028430093 0.00255680408795988 -0.0021683267
## AgeAccelPheno 0.0037546145 0.00263382056095098 -0.0014076738
## DNAmAgeSkinBloodClockAdjAge 0.0016390285 0.00216266982751781 -0.0025998044
## AgeAccelGrim 0.0039938498 0.00164896030877211 0.0007618876
## DNAmTLAdjAge -0.0001014871 0.000111871519540741 -0.0003207553
## IEAA -0.0021285213 0.00269473488082702 -0.0074102016
## EEAA 0.0037313699 0.00327771758612151 -0.0026929566
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.0040079366 0.576360404235931 > 0.05
## AgeAccelerationResidualHannum 0.0078543454 0.266164520047206 > 0.05
## AgeAccelPheno 0.0089169028 0.154001391118467 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.0058778613 0.448527185647545 > 0.05
## AgeAccelGrim 0.0072258120 0.0154335997193696 <= 0.05
## DNAmTLAdjAge 0.0001177811 0.36431393900575 > 0.05
## IEAA 0.0031531591 0.42959697033491 > 0.05
## EEAA 0.0101556963 0.25495143497931 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Likelihood ratio (LR) test (no confounders)
Full model: \[
Y = \beta_0 + \beta_1 * cum\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.6262 0.6842
## Hannum EAA 0.3685 0.6842
## PhenoAge EAA 0.1953 0.6842
## Skin&Blood EAA 0.5237 0.6842
## GrimAge EAA 0.0350 0.2800
## DNAmTL 0.6842 0.6842
## IEAA 0.4877 0.6842
## EEAA 0.3180 0.6842
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[
Y = \beta_0 + \beta_1 * cum\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.7033 0.7033
## Hannum EAA 0.3100 0.4960
## PhenoAge EAA 0.0621 0.2484
## Skin&Blood EAA 0.1114 0.2971
## GrimAge EAA 0.0300 0.2400
## DNAmTL 0.5423 0.6198
## IEAA 0.5021 0.6198
## EEAA 0.2754 0.4960
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[
Y = \beta_0 + \beta_1 * cum\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.9653 0.9946
## Hannum EAA 0.1054 0.2502
## PhenoAge EAA 0.0318 0.1792
## Skin&Blood EAA 0.1487 0.2502
## GrimAge EAA 0.0448 0.1792
## DNAmTL 0.6183 0.8244
## IEAA 0.9946 0.9946
## EEAA 0.1564 0.2502
4.3. Childhood exposure to 5MC
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| bir_5mc |
4.83 (2.6, 8.2) |
2.10 (1.5, 4.4) |
4.83 (3.0, 8.2) |
8.02 (3.7, 8.8) |
| (Missing) |
3 |
2 |
1 |
0 |
Primary analysis
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{childhood_5mc} \\
& + \beta_{2} * county + \beta_{3} * BMI + \beta_{4} * ses +
\beta_{5} * edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.9511 0.9511
## Hannum EAA 0.1340 0.2144
## PhenoAge EAA 0.0176 0.0704
## Skin&Blood EAA 0.0984 0.1968
## GrimAge EAA 0.0004 0.0032
## DNAmTL 0.8526 0.9511
## IEAA 0.3279 0.4372
## EEAA 0.0790 0.1968
GEE (mix)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *childhood\_5mc\\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.002549647 0.175648408840649 -0.34172123
## AgeAccelerationResidualHannum 0.214124879 0.156901216094478 -0.09340150
## AgeAccelPheno 0.328090148 0.15503211923717 0.02422719
## DNAmAgeSkinBloodClockAdjAge 0.184739910 0.127394623047048 -0.06495355
## AgeAccelGrim 0.316489482 0.0963285908716984 0.12768544
## DNAmTLAdjAge -0.003382397 0.00704699006180516 -0.01719450
## IEAA -0.172753745 0.164532364342251 -0.49523718
## EEAA 0.313411448 0.201414432212561 -0.08136084
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.3468205 0.988418609834732 > 0.05
## AgeAccelerationResidualHannum 0.5216513 0.172343774292717 > 0.05
## AgeAccelPheno 0.6319531 0.0343216724056407 <= 0.05
## DNAmAgeSkinBloodClockAdjAge 0.4344334 0.147019764983751 > 0.05
## AgeAccelGrim 0.5052935 0.00101794428051305 <= 0.01
## DNAmTLAdjAge 0.0104297 0.631243324757604 > 0.05
## IEAA 0.1497297 0.293732747964983 > 0.05
## EEAA 0.7081837 0.119695588251105 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Sensitivity analysis
GEE (no confounders)
In the following section, we performed generalized estimating
equations (GEE) with equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *childhood\_5mc + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations.
Results:
## [1] " Result data: "
## coefficient std ci_lower
## AgeAccelerationResidual 0.019159838 0.119620299681397 -0.21529595
## AgeAccelerationResidualHannum -0.041572743 0.10121495306854 -0.23995405
## AgeAccelPheno 0.063948683 0.100238231551026 -0.13251825
## DNAmAgeSkinBloodClockAdjAge 0.104833952 0.075228712841312 -0.04261433
## AgeAccelGrim 0.149523037 0.0565650873465267 0.03865547
## DNAmTLAdjAge -0.003814336 0.00353079663956072 -0.01073470
## IEAA 0.001582042 0.127834093339104 -0.24897278
## EEAA -0.053365322 0.132090247562408 -0.31226221
## ci_upper p_val sig_level
## AgeAccelerationResidual 0.253615625 0.872745484941928 > 0.05
## AgeAccelerationResidualHannum 0.156808565 0.681265269321088 > 0.05
## AgeAccelPheno 0.260415616 0.523495170158012 > 0.05
## DNAmAgeSkinBloodClockAdjAge 0.252282229 0.163457636388872 > 0.05
## AgeAccelGrim 0.260390608 0.00820827841291805 <= 0.01
## DNAmTLAdjAge 0.003106025 0.280006515297229 > 0.05
## IEAA 0.252136865 0.990125837758072 > 0.05
## EEAA 0.205531563 0.686207920603063 > 0.05
## EAAs
## AgeAccelerationResidual Horvath EAA
## AgeAccelerationResidualHannum Hannum EAA
## AgeAccelPheno PhenoAge EAA
## DNAmAgeSkinBloodClockAdjAge Skin&Blood EAA
## AgeAccelGrim GrimAge EAA
## DNAmTLAdjAge DNAmTL
## IEAA IEAA
## EEAA EEAA
Likelihood ratio (LR) test (no confounders)
Full model: \[
Y = \beta_0 + \beta_1 * childhood\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.8121 0.8478
## Hannum EAA 0.2726 0.4212
## PhenoAge EAA 0.0454 0.1816
## Skin&Blood EAA 0.1684 0.4060
## GrimAge EAA 0.0052 0.0416
## DNAmTL 0.8478 0.8478
## IEAA 0.3159 0.4212
## EEAA 0.2030 0.4060
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[
Y = \beta_0 + \beta_1 * childhood\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.5762 0.6585
## Hannum EAA 0.2975 0.3967
## PhenoAge EAA 0.0478 0.1803
## Skin&Blood EAA 0.0676 0.1803
## GrimAge EAA 0.0070 0.0560
## DNAmTL 0.8067 0.8067
## IEAA 0.1818 0.3636
## EEAA 0.2303 0.3685
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[
Y = \beta_0 + \beta_1 * childhood\_5mc + \epsilon
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.7809 0.7809
## Hannum EAA 0.1261 0.2466
## PhenoAge EAA 0.0323 0.1292
## Skin&Blood EAA 0.1005 0.2466
## GrimAge EAA 0.0086 0.0688
## DNAmTL 0.6247 0.7139
## IEAA 0.4805 0.6407
## EEAA 0.1541 0.2466
5. Ambient Exposure
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| bap_air |
39.44 (18.9, 74.1) |
10.09 (4.5, 20.7) |
45.22 (21.9, 76.7) |
69.11 (57.0, 131.2) |
| (Missing) |
4 |
0 |
3 |
1 |
| pm25_air |
139.32 (100.1, 227.1) |
120.16 (102.4, 160.7) |
137.48 (98.3, 211.0) |
421.89 (252.7, 480.4) |
| ANY_air |
564.51 (305.8, 977.5) |
477.86 (187.2, 791.4) |
560.77 (306.0, 914.7) |
7,030.90 (3,125.6, 10,967.7) |
| (Missing) |
35 |
7 |
24 |
4 |
| BPE_air |
46.55 (19.5, 73.4) |
12.70 (3.9, 19.7) |
48.29 (22.9, 83.4) |
66.81 (42.4, 114.8) |
| (Missing) |
4 |
0 |
3 |
1 |
| BaA_air |
40.51 (16.7, 88.1) |
9.44 (2.9, 23.3) |
50.23 (20.7, 106.2) |
68.31 (61.8, 163.2) |
| (Missing) |
4 |
0 |
3 |
1 |
| BbF_air |
62.69 (32.8, 120.9) |
31.76 (13.5, 50.1) |
65.78 (34.5, 124.7) |
88.69 (78.2, 181.6) |
| (Missing) |
4 |
0 |
3 |
1 |
| BkF_air |
13.24 (6.4, 25.9) |
3.37 (2.0, 7.6) |
15.07 (8.0, 28.6) |
27.64 (12.5, 48.0) |
| (Missing) |
4 |
0 |
3 |
1 |
| CHR_air |
45.82 (16.4, 86.9) |
15.24 (4.9, 31.8) |
50.79 (18.1, 86.9) |
91.89 (61.3, 134.8) |
| (Missing) |
4 |
0 |
3 |
1 |
| DBA_air |
12.49 (4.4, 27.5) |
3.92 (1.4, 11.0) |
14.25 (6.1, 31.8) |
12.67 (7.6, 25.3) |
| (Missing) |
4 |
0 |
3 |
1 |
| FLT_air |
17.33 (5.1, 41.6) |
4.35 (0.6, 7.2) |
19.15 (6.5, 41.8) |
104.71 (48.9, 175.2) |
| (Missing) |
4 |
0 |
3 |
1 |
| FLU_air |
276.10 (165.2, 546.9) |
251.42 (219.0, 298.2) |
276.10 (159.0, 544.6) |
1,426.05 (632.8, 2,241.9) |
| (Missing) |
35 |
7 |
24 |
4 |
| IPY_air |
27.29 (14.0, 47.7) |
12.70 (4.3, 16.6) |
30.70 (15.3, 48.1) |
69.17 (51.1, 118.8) |
| (Missing) |
4 |
0 |
3 |
1 |
| NAP_air |
3,170.67 (1,807.5, 5,568.9) |
3,217.69 (2,288.3, 4,623.5) |
3,142.04 (1,759.1, 5,442.8) |
29,828.64 (11,068.1, 49,775.1) |
| (Missing) |
35 |
7 |
24 |
4 |
| PHE_air |
396.14 (220.9, 820.9) |
363.30 (294.3, 550.4) |
380.03 (206.2, 771.8) |
2,120.65 (907.6, 3,404.2) |
| (Missing) |
35 |
7 |
24 |
4 |
| PYR_air |
21.81 (6.1, 51.3) |
6.42 (0.6, 8.2) |
23.96 (7.7, 51.3) |
108.99 (71.5, 191.4) |
| (Missing) |
4 |
0 |
3 |
1 |
Primary analysis
GEE for each ambient exposure measurement (mix model)
In the following section, we performed linear regression with
equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *X\\
& + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 *
edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations, and \(X\)
is one of the ambient exposure measurements.
The estimations of \(\beta_1\) with
given \(Y\) and \(X\) are shown below, which can be
interpreted as “the mean of Y changes given a one-unit increase in X
while holding other variables constant”.
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} +
\beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\
& + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 *
\text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\
& + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13}
* \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\
& + \beta_{16} * county + \beta_{17} * BMI + \beta_{18} * ses +
\beta_{19} * edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\] \(H_0\): The full model and
the nested model fit the data equally well. Thus, you should use the
nested model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0119 0.0476
## Hannum EAA 0.2902 0.3317
## PhenoAge EAA 0.0990 0.1980
## Skin&Blood EAA 0.1515 0.2424
## GrimAge EAA 0.0018 0.0144
## DNAmTL 0.4917 0.4917
## IEAA 0.0335 0.0893
## EEAA 0.2244 0.2992
Sensitivity analysis
GEE for each ambient exposure measurement (no confounders)
In the following section, we performed linear regression with
equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations, and \(X\)
is one of the ambient exposure measurements.
The estimations of \(\beta_1\) with
given \(Y\) and \(X\) are shown below, which can be
interpreted as “the mean of Y changes given a one-unit increase in X
while holding other variables constant”.
Likelihood ratio (LR) test (no confounders)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} +
\beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\
& + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 *
\text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\
& + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13}
* \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\
& + \epsilon
\end{aligned}
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0124 0.0440
## Hannum EAA 0.2385 0.3023
## PhenoAge EAA 0.0864 0.1728
## Skin&Blood EAA 0.2439 0.3023
## GrimAge EAA 0.0165 0.0440
## DNAmTL 0.5855 0.5855
## IEAA 0.0154 0.0440
## EEAA 0.2645 0.3023
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} +
\beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\
& + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 *
\text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\
& + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13}
* \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\
& + \epsilon
\end{aligned}
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.4481 0.9041
## Hannum EAA 0.6843 0.9041
## PhenoAge EAA 0.6391 0.9041
## Skin&Blood EAA 0.9041 0.9041
## GrimAge EAA 0.1945 0.9041
## DNAmTL 0.8384 0.9041
## IEAA 0.2352 0.9041
## EEAA 0.6906 0.9041
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} +
\beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\
& + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 *
\text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\
& + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13}
* \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\
& + \epsilon
\end{aligned}
\]
Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.7795 0.9656
## Hannum EAA 0.8191 0.9656
## PhenoAge EAA 0.4285 0.9656
## Skin&Blood EAA 0.8324 0.9656
## GrimAge EAA 0.0552 0.4416
## DNAmTL 0.9656 0.9656
## IEAA 0.3845 0.9656
## EEAA 0.8923 0.9656
6. Urinary Measurements
Summary the exposure estimates:
| Characteristic |
Overall, N = 112 |
Smokeles, N = 17 |
Smoky, N = 87 |
Wood_and_or_Plant, N = 8 |
| Benzanthracene_Chrysene_urine |
0.38 (0.3, 0.8) |
0.29 (0.1, 0.6) |
0.45 (0.3, 1.0) |
0.36 (0.3, 0.6) |
| (Missing) |
2 |
0 |
2 |
0 |
| Naphthalene_urine |
107.58 (72.1, 168.8) |
96.94 (54.9, 110.9) |
108.85 (73.5, 169.3) |
141.97 (99.7, 174.6) |
| Methylnaphthalene_2_urine |
26.67 (17.9, 45.0) |
17.92 (8.8, 23.4) |
30.18 (20.9, 46.4) |
20.30 (12.2, 34.2) |
| (Missing) |
7 |
0 |
7 |
0 |
| Methylnaphthalene_1_urine |
10.93 (6.6, 18.1) |
5.26 (3.6, 10.5) |
11.52 (7.7, 20.9) |
15.06 (11.0, 26.7) |
| (Missing) |
4 |
1 |
3 |
0 |
| Acenaphthene_urine |
3.14 (2.2, 7.3) |
2.82 (2.2, 3.5) |
3.38 (2.3, 7.9) |
3.58 (2.0, 7.2) |
| Phenanthrene_Anthracene_urine |
112.78 (42.4, 239.6) |
78.75 (41.6, 135.5) |
115.58 (56.8, 239.7) |
109.86 (39.6, 305.8) |
| Fluoranthene_urine |
16.53 (6.1, 23.1) |
17.68 (5.4, 20.8) |
15.25 (6.3, 23.2) |
23.23 (22.4, 36.0) |
| Pyrene_urine |
0.54 (0.4, 0.8) |
0.41 (0.4, 0.4) |
0.54 (0.4, 0.8) |
0.78 (0.7, 0.9) |
| (Missing) |
15 |
7 |
7 |
1 |
Primary analysis
GEE for each ambient exposure measurement (mix)
In the following section, we performed linear regression with
equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *X \\
& + \beta_{2} * county + \beta_{3} * BMI + \beta_{4} * ses +
\beta_{5} * edu + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations, and \(X\)
is one of the urinary exposure measurements.
Results:
Likelihood ratio (LR) test (mix model)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2
* \text{Naphthalene} \\
& + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 *
\text{1.Methylnaphthalene} \\
& + \beta_5 * \text{Acenaphthene }+ \beta_6 *
\text{Phenanthrene_Anthracene} \\
& + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 *
\text{Fluoranthene} + \beta_9 * \text{Pyrene} \\
& + \beta_{10} * county + \beta_{11} * BMI + \beta_{12} * ses +
\beta_{13} * edu + \epsilon
\end{aligned}
\] Nested model: \[
\begin{aligned}
Y = & \beta_0 \\
& + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 *
edu + \epsilon
\end{aligned}
\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0006 0.0012
## Hannum EAA 0.0221 0.0295
## PhenoAge EAA 0.0001 0.0008
## Skin&Blood EAA 0.0011 0.0018
## GrimAge EAA 0.0003 0.0008
## DNAmTL 0.0312 0.0357
## IEAA 0.0002 0.0008
## EEAA 0.0504 0.0504
Sensitivity analysis
GEE for each ambient exposure measurement (no confounders)
In the following section, we performed linear regression with
equation \[
\begin{aligned}
Y = & \beta_0 + \beta_1 *X + \epsilon
\end{aligned}
\] where \(Y\) is one of the
epigenetic age accelerations, and \(X\)
is one of the urinary exposure measurements.
Results:
Likelihood ratio (LR) test (no confounders)
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2
* \text{Naphthalene} \\
& + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 *
\text{1.Methylnaphthalene} \\
& + \beta_5 * \text{Acenaphthene }+ \beta_6 *
\text{Phenanthrene_Anthracene} \\
& + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 *
\text{Fluoranthene} + \beta_9 * \text{Pyrene} \\
& + \epsilon
\end{aligned}
\] Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0012 0.0048
## Hannum EAA 0.0860 0.0983
## PhenoAge EAA 0.0009 0.0048
## Skin&Blood EAA 0.0075 0.0150
## GrimAge EAA 0.0145 0.0232
## DNAmTL 0.0342 0.0456
## IEAA 0.0029 0.0077
## EEAA 0.1986 0.1986
Likelihood ratio (LR) test (no confounders) with subjects using only
smoky or smokeless coal
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2
* \text{Naphthalene} \\
& + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 *
\text{1.Methylnaphthalene} \\
& + \beta_5 * \text{Acenaphthene }+ \beta_6 *
\text{Phenanthrene_Anthracene} \\
& + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 *
\text{Fluoranthene} + \beta_9 * \text{Pyrene} \\
& + \epsilon
\end{aligned}
\] Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.0318 0.0636
## Hannum EAA 0.1305 0.1959
## PhenoAge EAA 0.0049 0.0196
## Skin&Blood EAA 0.0009 0.0072
## GrimAge EAA 0.4144 0.4144
## DNAmTL 0.1663 0.1959
## IEAA 0.0170 0.0453
## EEAA 0.1714 0.1959
Likelihood ratio (LR) test (no confounders) with subjects only using
smoky coal
Full model: \[
\begin{aligned}
Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2
* \text{Naphthalene} \\
& + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 *
\text{1.Methylnaphthalene} \\
& + \beta_5 * \text{Acenaphthene }+ \beta_6 *
\text{Phenanthrene_Anthracene} \\
& + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 *
\text{Fluoranthene} + \beta_9 * \text{Pyrene} \\
& + \epsilon
\end{aligned}
\] Nested model: \[Y = \beta_0 +
\epsilon\]
\(H_0\): The full model and the
nested model fit the data equally well. Thus, you should use the nested
model.
\(H_A\): The full model fits the data
significantly better than the nested model. Thus, you should use the
full model.
P-values results:
## p_vals p_vals_BHadj
## Horvath EAA 0.3420 0.4325
## Hannum EAA 0.3784 0.4325
## PhenoAge EAA 0.0197 0.1076
## Skin&Blood EAA 0.1371 0.3656
## GrimAge EAA 0.4957 0.4957
## DNAmTL 0.2194 0.4325
## IEAA 0.0269 0.1076
## EEAA 0.3324 0.4325